├── Inf-Net-master ├── .gitignore ├── Code │ ├── __init__.py │ ├── model_lung_infection │ │ ├── InfNet_Res2Net.py │ │ ├── InfNet_ResNet.py │ │ ├── InfNet_UNet.py │ │ ├── InfNet_VGGNet.py │ │ ├── __init__.py │ │ ├── backbone │ │ │ ├── DenseNet.py │ │ │ ├── Res2Net.py │ │ │ ├── ResNet.py │ │ │ ├── VGGNet.py │ │ │ └── __init__.py │ │ └── module │ │ │ ├── __init__.py │ │ │ ├── grid_attention_layer.py │ │ │ ├── networks_other.py │ │ │ └── unet_parts.py │ └── utils │ │ ├── __init__.py │ │ ├── dataloader_LungInf.py │ │ ├── dataloader_MulClsLungInf_UNet.py │ │ ├── dirtree.txt │ │ ├── format_conversion.py │ │ ├── generate_file_tree.py │ │ ├── label2Img.py │ │ ├── loss_function.py │ │ ├── onehot.py │ │ ├── split_1600.py │ │ ├── split_class.py │ │ └── utils.py ├── Docs │ └── Using Inf-Net in Google Colab- A Debugging Guideline.pdf ├── EvaluateResults │ ├── LungInfection-Test-1-mat │ │ ├── Inf-Net.mat │ │ └── Semi-Inf-Net.mat │ ├── LungInfection-Test-1_result.txt │ ├── MultiClassInfection-Test-1-mat │ │ └── Semi-Inf-Net_UNet.mat │ ├── MultiClassInfection-Test-1_result.txt │ ├── MultiClassInfection-Test-2-mat │ │ └── Semi-Inf-Net_UNet.mat │ ├── MultiClassInfection-Test-2_result.txt │ └── previous │ │ ├── LungInfection-Test-1-mat │ │ ├── Inf-Net.mat │ │ ├── Semi-Inf-Net.mat │ │ ├── UNet++.mat │ │ └── UNet.mat │ │ ├── LungInfection-Test-1_result.txt │ │ ├── MultiClassInfection-Test-1-mat │ │ ├── DeepLabV3Plus_Stride16.mat │ │ ├── DeepLabV3Plus_Stride8.mat │ │ ├── FCN8s_1100.mat │ │ └── Semi-Inf-Net_FCN8s_1100.mat │ │ ├── MultiClassInfection-Test-1_result.txt │ │ ├── MultiClassInfection-Test-2-mat │ │ ├── DeepLabV3Plus_Stride16.mat │ │ ├── DeepLabV3Plus_Stride8.mat │ │ ├── FCN8s_1100.mat │ │ └── Semi-Inf-Net_FCN8s_1100.mat │ │ └── MultiClassInfection-Test-2_result.txt ├── EvaluationTool │ ├── CalMAE.m │ ├── Enhancedmeasure.m │ ├── Fmeasure_calu.m │ ├── S_object.m │ ├── S_region.m │ ├── StructureMeasure.m │ ├── main.m │ └── original_WFb.m ├── LICENSE ├── MyTest_LungInf.py ├── MyTest_MulClsLungInf_UNet.py ├── MyTrain_LungInf.py ├── MyTrain_MulClsLungInf_UNet.py ├── PseudoGenerator.py ├── README.md └── requirements.txt ├── PraNet-master ├── MyTest.py ├── MyTrain.py ├── README.md ├── eval │ ├── CalMAE.m │ ├── Enhancedmeasure.m │ ├── Fmeasure_calu.m │ ├── README.md │ ├── S_object.m │ ├── S_region.m │ ├── StructureMeasure.m │ ├── main.m │ └── original_WFb.m ├── imgs │ ├── PraNet-Award.png │ ├── framework-final-min.png │ ├── qualitative_results.png │ ├── quantiative_results_1.png │ └── quantiative_results_2.png ├── jittor │ ├── MyTest.py │ ├── README.md │ ├── lib │ │ ├── PraNet_Res2Net.py │ │ └── Res2Net_v1b.py │ └── utils │ │ └── dataloader.py ├── lib │ ├── PraNet_Res2Net.py │ ├── PraNet_ResNet.py │ ├── Res2Net_v1b.py │ └── ResNet.py └── utils │ ├── __pycache__ │ ├── dataloader.cpython-36.pyc │ └── utils.cpython-36.pyc │ ├── dataloader.py │ ├── format_conversion.py │ └── utils.py ├── README.md ├── TBraTS-main ├── .idea │ ├── .gitignore │ ├── TBraTS-main.iml │ ├── inspectionProfiles │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── misc.xml │ └── modules.xml ├── README.md ├── binary.py ├── checkpoint │ └── nullfile ├── data │ ├── BraTS2019 │ └── preprocessBraTS.py ├── image │ ├── F1N.png │ ├── Trust_E.gif │ └── nullfile ├── log │ └── nullfile ├── models │ ├── criterions.py │ ├── lib │ │ ├── IntmdSequential.py │ │ ├── PositionalEncoding.py │ │ ├── TransBTS_downsample8x_skipconnection.py │ │ ├── Transformer.py │ │ ├── UNet3DZoo.py │ │ ├── Unet_skipconnection.py │ │ ├── VNet3D.py │ │ ├── nullfile │ │ └── seg_eval.py │ └── trustedseg.py ├── numpyfunctions.py ├── plot.py ├── predict.py ├── results │ └── nullfile ├── test_uncertainty.py ├── train.py └── trainTBraTS.py ├── UMIS-main ├── .idea │ ├── .gitignore │ ├── UMIS-main.iml │ ├── inspectionProfiles │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── misc.xml │ └── modules.xml └── UMIS-main │ ├── README.md │ ├── binary.py │ ├── checkpoint │ └── null │ ├── data │ ├── ISIC2018.py │ ├── null │ └── transform.py │ ├── image │ ├── Moti-TMIS.png │ ├── NC_F1.png │ ├── brats_fA2.png │ └── null │ ├── log │ └── null │ ├── models │ ├── TrustworthySeg.py │ ├── criterions.py │ └── lib │ │ ├── IntmdSequential.py │ │ ├── NetZoo.py │ │ ├── PositionalEncoding.py │ │ ├── TransU_zoo.py │ │ ├── Transformer.py │ │ ├── UNet2DZoo.py │ │ ├── UNet3DZoo.py │ │ ├── Unet_skipconnection.py │ │ ├── VNet2D.py │ │ ├── VNet3D.py │ │ ├── null │ │ ├── probabilistic_unet.py │ │ ├── probabilistic_unet2D.py │ │ ├── unet.py │ │ ├── unet2D.py │ │ ├── unet_blocks.py │ │ ├── unet_blocks2D.py │ │ ├── utils.py │ │ ├── utils2D.py │ │ ├── vit_seg_configs.py │ │ ├── vit_seg_modeling.py │ │ └── vit_seg_modeling_resnet_skip.py │ ├── numpyfunctions.py │ ├── plot.py │ ├── predict.py │ ├── pretrainUMIS.py │ ├── results │ └── null │ └── trainUMIS.py ├── UNetPLusPlus-master ├── .DS_Store ├── LICENSE ├── README.md ├── nnunet │ ├── __init__.py │ ├── configuration.py │ ├── dataset_conversion │ │ ├── Task017_BeyondCranialVaultAbdominalOrganSegmentation.py │ │ ├── Task024_Promise2012.py │ │ ├── Task027_AutomaticCardiacDetectionChallenge.py │ │ ├── Task029_LiverTumorSegmentationChallenge.py │ │ ├── Task032_BraTS_2018.py │ │ ├── Task035_ISBI_MSLesionSegmentationChallenge.py │ │ ├── Task037_038_Chaos_Challenge.py │ │ ├── Task040_KiTS.py │ │ ├── Task043_BraTS_2019.py │ │ ├── Task055_SegTHOR.py │ │ ├── Task056_VerSe2019.py │ │ ├── Task056_Verse_normalize_orientation.py │ │ ├── Task058_ISBI_EM_SEG.py │ │ ├── Task059_EPFL_EM_MITO_SEG.py │ │ ├── Task061_CREMI.py │ │ ├── Task062_NIHPancreas.py │ │ ├── Task064_KiTS_labelsFixed.py │ │ ├── Task065_KiTS_NicksLabels.py │ │ ├── Task069_CovidSeg.py │ │ ├── Task075_Fluo_C3DH_A549_ManAndSim.py │ │ ├── Task076_Fluo_N3DH_SIM.py │ │ ├── Task082_BraTS_2020.py │ │ ├── Task089_Fluo-N2DH-SIM.py │ │ └── __init__.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── add_dummy_task_with_mean_over_all_tasks.py │ │ ├── add_mean_dice_to_json.py │ │ ├── collect_results_files.py │ │ ├── evaluator.py │ │ ├── metrics.py │ │ ├── model_selection │ │ │ ├── __init__.py │ │ │ ├── collect_all_fold0_results_and_summarize_in_one_csv.py │ │ │ ├── ensemble.py │ │ │ ├── figure_out_what_to_submit.py │ │ │ ├── rank_candidates.py │ │ │ ├── rank_candidates_StructSeg.py │ │ │ ├── rank_candidates_cascade.py │ │ │ ├── summarize_results_in_one_json.py │ │ │ └── summarize_results_with_plans.py │ │ ├── region_based_evaluation.py │ │ └── surface_dice.py │ ├── experiment_planning │ │ ├── DatasetAnalyzer.py │ │ ├── __init__.py │ │ ├── alternative_experiment_planning │ │ │ ├── __init__.py │ │ │ ├── experiment_planner_baseline_3DUNet_v21_11GB.py │ │ │ ├── experiment_planner_baseline_3DUNet_v21_32GB.py │ │ │ ├── experiment_planner_baseline_3DUNet_v21_3convperstage.py │ │ │ ├── experiment_planner_baseline_3DUNet_v22.py │ │ │ ├── experiment_planner_baseline_3DUNet_v23.py │ │ │ ├── experiment_planner_residual_3DUNet_v21.py │ │ │ ├── normalization │ │ │ │ ├── __init__.py │ │ │ │ ├── experiment_planner_3DUNet_CT2.py │ │ │ │ └── experiment_planner_3DUNet_nonCT.py │ │ │ ├── patch_size │ │ │ │ ├── __init__.py │ │ │ │ ├── experiment_planner_3DUNet_isotropic_in_mm.py │ │ │ │ └── experiment_planner_3DUNet_isotropic_in_voxels.py │ │ │ ├── pooling_and_convs │ │ │ │ ├── __init__.py │ │ │ │ ├── experiment_planner_baseline_3DUNet_allConv3x3.py │ │ │ │ └── experiment_planner_baseline_3DUNet_poolBasedOnSpacing.py │ │ │ ├── readme.md │ │ │ └── target_spacing │ │ │ │ ├── __init__.py │ │ │ │ └── experiment_planner_baseline_3DUNet_targetSpacingForAnisoAxis.py │ │ ├── change_batch_size.py │ │ ├── common_utils.py │ │ ├── experiment_planner_baseline_2DUNet.py │ │ ├── experiment_planner_baseline_2DUNet_v21.py │ │ ├── experiment_planner_baseline_3DUNet.py │ │ ├── experiment_planner_baseline_3DUNet_v21.py │ │ ├── nnUNet_convert_decathlon_task.py │ │ ├── nnUNet_plan_and_preprocess.py │ │ ├── old │ │ │ ├── __init__.py │ │ │ └── old_plan_and_preprocess_task.py │ │ ├── summarize_plans.py │ │ └── utils.py │ ├── inference │ │ ├── __init__.py │ │ ├── change_trainer.py │ │ ├── ensemble_predictions.py │ │ ├── predict.py │ │ ├── predict_simple.py │ │ ├── pretrained_models │ │ │ ├── __init__.py │ │ │ ├── collect_pretrained_models.py │ │ │ └── download_pretrained_model.py │ │ └── segmentation_export.py │ ├── network_architecture │ │ ├── __init__.py │ │ ├── custom_modules │ │ │ ├── __init__.py │ │ │ ├── conv_blocks.py │ │ │ ├── feature_response_normalization.py │ │ │ ├── helperModules.py │ │ │ └── mish.py │ │ ├── generic_UNet.py │ │ ├── generic_UNetPlusPlus.py │ │ ├── generic_UNet_DP.py │ │ ├── generic_XNet.py │ │ ├── generic_hipp_XNet.py │ │ ├── generic_modular_UNet.py │ │ ├── generic_modular_residual_UNet.py │ │ ├── initialization.py │ │ └── neural_network.py │ ├── paths.py │ ├── postprocessing │ │ ├── __init__.py │ │ ├── connected_components.py │ │ ├── consolidate_all_for_paper.py │ │ ├── consolidate_postprocessing.py │ │ └── consolidate_postprocessing_simple.py │ ├── preprocessing │ │ ├── __init__.py │ │ ├── cropping.py │ │ ├── preprocessing.py │ │ └── sanity_checks.py │ ├── run │ │ ├── __init__.py │ │ ├── default_configuration.py │ │ ├── run_training.py │ │ ├── run_training_DDP.py │ │ └── run_training_DP.py │ ├── training │ │ ├── __init__.py │ │ ├── cascade_stuff │ │ │ ├── __init__.py │ │ │ └── predict_next_stage.py │ │ ├── data_augmentation │ │ │ ├── __init__.py │ │ │ ├── custom_transforms.py │ │ │ ├── default_data_augmentation.py │ │ │ ├── downsampling.py │ │ │ └── pyramid_augmentations.py │ │ ├── dataloading │ │ │ ├── __init__.py │ │ │ └── dataset_loading.py │ │ ├── learning_rate │ │ │ ├── __init__.py │ │ │ └── poly_lr.py │ │ ├── loss_functions │ │ │ ├── TopK_loss.py │ │ │ ├── __init__.py │ │ │ ├── crossentropy.py │ │ │ ├── deep_supervision.py │ │ │ └── dice_loss.py │ │ ├── model_restore.py │ │ ├── network_training │ │ │ ├── BraTS_trainer │ │ │ │ ├── __init__.py │ │ │ │ └── nnUNetTrainerV2BraTSRegions.py │ │ │ ├── __init__.py │ │ │ ├── network_trainer.py │ │ │ ├── nnUNetPlusPlusTrainerV2.py │ │ │ ├── nnUNetTrainer.py │ │ │ ├── nnUNetTrainerCascadeFullRes.py │ │ │ ├── nnUNetTrainerV2.py │ │ │ ├── nnUNetTrainerV2_CascadeFullRes.py │ │ │ ├── nnUNetTrainerV2_DDP.py │ │ │ ├── nnUNetTrainerV2_DP.py │ │ │ ├── nnUNetTrainerV2_fp32.py │ │ │ └── nnUNet_variants │ │ │ │ ├── __init__.py │ │ │ │ ├── architectural_variants │ │ │ │ ├── __init__.py │ │ │ │ ├── nnUNetTrainerV2_3ConvPerStage.py │ │ │ │ ├── nnUNetTrainerV2_3ConvPerStage_samefilters.py │ │ │ │ ├── nnUNetTrainerV2_BN.py │ │ │ │ ├── nnUNetTrainerV2_FRN.py │ │ │ │ ├── nnUNetTrainerV2_GN.py │ │ │ │ ├── nnUNetTrainerV2_GeLU.py │ │ │ │ ├── nnUNetTrainerV2_LReLU_slope_2en1.py │ │ │ │ ├── nnUNetTrainerV2_Mish.py │ │ │ │ ├── nnUNetTrainerV2_NoNormalization.py │ │ │ │ ├── nnUNetTrainerV2_NoNormalization_lr1en3.py │ │ │ │ ├── nnUNetTrainerV2_ReLU.py │ │ │ │ ├── nnUNetTrainerV2_ReLU_biasInSegOutput.py │ │ │ │ ├── nnUNetTrainerV2_ReLU_convReLUIN.py │ │ │ │ ├── nnUNetTrainerV2_ResencUNet.py │ │ │ │ ├── nnUNetTrainerV2_allConv3x3.py │ │ │ │ ├── nnUNetTrainerV2_lReLU_biasInSegOutput.py │ │ │ │ ├── nnUNetTrainerV2_lReLU_convlReLUIN.py │ │ │ │ ├── nnUNetTrainerV2_noDeepSupervision.py │ │ │ │ └── nnUNetTrainerV2_softDeepSupervision.py │ │ │ │ ├── cascade │ │ │ │ ├── __init__.py │ │ │ │ ├── nnUNetTrainerV2CascadeFullRes_DAVariants.py │ │ │ │ ├── nnUNetTrainerV2CascadeFullRes_lowerLR.py │ │ │ │ ├── nnUNetTrainerV2CascadeFullRes_shorter.py │ │ │ │ └── nnUNetTrainerV2CascadeFullRes_shorter_lowerLR.py │ │ │ │ ├── copies │ │ │ │ ├── __init__.py │ │ │ │ └── nnUNetTrainerV2_copies.py │ │ │ │ ├── data_augmentation │ │ │ │ ├── __init__.py │ │ │ │ ├── nnUNetTrainerV2_DA2.py │ │ │ │ ├── nnUNetTrainerV2_DA3.py │ │ │ │ ├── nnUNetTrainerV2_independentScalePerAxis.py │ │ │ │ ├── nnUNetTrainerV2_insaneDA.py │ │ │ │ ├── nnUNetTrainerV2_noDA.py │ │ │ │ └── nnUNetTrainerV2_noMirroring.py │ │ │ │ ├── loss_function │ │ │ │ ├── __init__.py │ │ │ │ ├── nnUNetTrainerV2_ForceBD.py │ │ │ │ ├── nnUNetTrainerV2_ForceSD.py │ │ │ │ ├── nnUNetTrainerV2_Loss_CE.py │ │ │ │ ├── nnUNetTrainerV2_Loss_CEGDL.py │ │ │ │ ├── nnUNetTrainerV2_Loss_Dice.py │ │ │ │ ├── nnUNetTrainerV2_Loss_DiceTopK10.py │ │ │ │ ├── nnUNetTrainerV2_Loss_Dice_lr1en3.py │ │ │ │ ├── nnUNetTrainerV2_Loss_Dice_squared.py │ │ │ │ ├── nnUNetTrainerV2_Loss_MCC.py │ │ │ │ ├── nnUNetTrainerV2_Loss_TopK10.py │ │ │ │ ├── nnUNetTrainerV2_focalLoss.py │ │ │ │ └── nnUNetTrainerV2_graduallyTransitionFromCEToDice.py │ │ │ │ ├── miscellaneous │ │ │ │ ├── __init__.py │ │ │ │ └── nnUNetTrainerV2_fullEvals.py │ │ │ │ ├── nnUNetTrainerCE.py │ │ │ │ ├── nnUNetTrainerNoDA.py │ │ │ │ ├── optimizer_and_lr │ │ │ │ ├── __init__.py │ │ │ │ ├── nnUNetTrainerV2_Adam.py │ │ │ │ ├── nnUNetTrainerV2_Adam_ReduceOnPlateau.py │ │ │ │ ├── nnUNetTrainerV2_Adam_lr_3en4.py │ │ │ │ ├── nnUNetTrainerV2_Ranger_lr1en2.py │ │ │ │ ├── nnUNetTrainerV2_Ranger_lr3en3.py │ │ │ │ ├── nnUNetTrainerV2_Ranger_lr3en4.py │ │ │ │ ├── nnUNetTrainerV2_SGD_ReduceOnPlateau.py │ │ │ │ ├── nnUNetTrainerV2_SGD_fixedSchedule.py │ │ │ │ ├── nnUNetTrainerV2_SGD_fixedSchedule2.py │ │ │ │ ├── nnUNetTrainerV2_SGD_lrs.py │ │ │ │ ├── nnUNetTrainerV2_cycleAtEnd.py │ │ │ │ ├── nnUNetTrainerV2_fp16.py │ │ │ │ ├── nnUNetTrainerV2_momentum09.py │ │ │ │ ├── nnUNetTrainerV2_momentum095.py │ │ │ │ ├── nnUNetTrainerV2_momentum098.py │ │ │ │ ├── nnUNetTrainerV2_momentum09in2D.py │ │ │ │ ├── nnUNetTrainerV2_reduceMomentumDuringTraining.py │ │ │ │ └── nnUNetTrainerV2_warmup.py │ │ │ │ ├── profiling │ │ │ │ ├── __init__.py │ │ │ │ ├── nnUNetTrainerV2_2epochs.py │ │ │ │ └── nnUNetTrainerV2_dummyLoad.py │ │ │ │ └── resampling │ │ │ │ ├── __init__.py │ │ │ │ └── nnUNetTrainerV2_resample33.py │ │ └── optimizer │ │ │ ├── __init__.py │ │ │ └── ranger.py │ └── utilities │ │ ├── __init__.py │ │ ├── distributed.py │ │ ├── file_conversions.py │ │ ├── file_endings.py │ │ ├── folder_names.py │ │ ├── nd_softmax.py │ │ ├── one_hot_encoding.py │ │ ├── random_stuff.py │ │ ├── recursive_delete_npz.py │ │ ├── recursive_rename_taskXX_to_taskXXX.py │ │ ├── sitk_stuff.py │ │ ├── task_name_id_conversion.py │ │ ├── tensor_utilities.py │ │ └── to_torch.py ├── setup.cfg ├── setup.py └── tests │ └── test_steps_for_sliding_window_prediction.py ├── deeplab_v3 ├── README.md ├── deeplabv3_resnet50.png ├── get_palette.py ├── my_dataset.py ├── palette.json ├── pascal_voc_classes.json ├── predict.py ├── requirements.txt ├── results20211027-104607.txt ├── src │ ├── __init__.py │ ├── deeplabv3_model.py │ ├── mobilenet_backbone.py │ └── resnet_backbone.py ├── train.py ├── train_multi_GPU.py ├── train_utils │ ├── __init__.py │ ├── distributed_utils.py │ └── train_and_eval.py ├── transforms.py └── validation.py ├── fcn ├── .idea │ ├── .gitignore │ ├── fcn.iml │ ├── inspectionProfiles │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── misc.xml │ └── modules.xml ├── README.md ├── __pycache__ │ ├── my_dataset.cpython-38.pyc │ └── transforms.cpython-38.pyc ├── data │ └── DRIVE │ │ ├── test │ │ ├── 1st_manual │ │ │ ├── 01_manual1.gif │ │ │ ├── 02_manual1.gif │ │ │ ├── 03_manual1.gif │ │ │ ├── 04_manual1.gif │ │ │ ├── 05_manual1.gif │ │ │ ├── 06_manual1.gif │ │ │ ├── 07_manual1.gif │ │ │ ├── 08_manual1.gif │ │ │ ├── 09_manual1.gif │ │ │ ├── 10_manual1.gif │ │ │ ├── 11_manual1.gif │ │ │ ├── 12_manual1.gif │ │ │ ├── 13_manual1.gif │ │ │ ├── 14_manual1.gif │ │ │ ├── 15_manual1.gif │ │ │ ├── 16_manual1.gif │ │ │ ├── 17_manual1.gif │ │ │ ├── 18_manual1.gif │ │ │ ├── 19_manual1.gif │ │ │ └── 20_manual1.gif │ │ ├── 2nd_manual │ │ │ ├── 01_manual2.gif │ │ │ ├── 02_manual2.gif │ │ │ ├── 03_manual2.gif │ │ │ ├── 04_manual2.gif │ │ │ ├── 05_manual2.gif │ │ │ ├── 06_manual2.gif │ │ │ ├── 07_manual2.gif │ │ │ ├── 08_manual2.gif │ │ │ ├── 09_manual2.gif │ │ │ ├── 10_manual2.gif │ │ │ ├── 11_manual2.gif │ │ │ ├── 12_manual2.gif │ │ │ ├── 13_manual2.gif │ │ │ ├── 14_manual2.gif │ │ │ ├── 15_manual2.gif │ │ │ ├── 16_manual2.gif │ │ │ ├── 17_manual2.gif │ │ │ ├── 18_manual2.gif │ │ │ ├── 19_manual2.gif │ │ │ └── 20_manual2.gif │ │ ├── images │ │ │ ├── 01_test.tif │ │ │ ├── 02_test.tif │ │ │ ├── 03_test.tif │ │ │ ├── 04_test.tif │ │ │ ├── 05_test.tif │ │ │ ├── 06_test.tif │ │ │ ├── 07_test.tif │ │ │ ├── 08_test.tif │ │ │ ├── 09_test.tif │ │ │ ├── 10_test.tif │ │ │ ├── 11_test.tif │ │ │ ├── 12_test.tif │ │ │ ├── 13_test.tif │ │ │ ├── 14_test.tif │ │ │ ├── 15_test.tif │ │ │ ├── 16_test.tif │ │ │ ├── 17_test.tif │ │ │ ├── 18_test.tif │ │ │ ├── 19_test.tif │ │ │ └── 20_test.tif │ │ └── mask │ │ │ ├── 01_test_mask.gif │ │ │ ├── 02_test_mask.gif │ │ │ ├── 03_test_mask.gif │ │ │ ├── 04_test_mask.gif │ │ │ ├── 05_test_mask.gif │ │ │ ├── 06_test_mask.gif │ │ │ ├── 07_test_mask.gif │ │ │ ├── 08_test_mask.gif │ │ │ ├── 09_test_mask.gif │ │ │ ├── 10_test_mask.gif │ │ │ ├── 11_test_mask.gif │ │ │ ├── 12_test_mask.gif │ │ │ ├── 13_test_mask.gif │ │ │ ├── 14_test_mask.gif │ │ │ ├── 15_test_mask.gif │ │ │ ├── 16_test_mask.gif │ │ │ ├── 17_test_mask.gif │ │ │ ├── 18_test_mask.gif │ │ │ ├── 19_test_mask.gif │ │ │ └── 20_test_mask.gif │ │ └── training │ │ ├── 1st_manual │ │ ├── 21_manual1.gif │ │ ├── 22_manual1.gif │ │ ├── 23_manual1.gif │ │ ├── 24_manual1.gif │ │ ├── 25_manual1.gif │ │ ├── 26_manual1.gif │ │ ├── 27_manual1.gif │ │ ├── 28_manual1.gif │ │ ├── 29_manual1.gif │ │ ├── 30_manual1.gif │ │ ├── 31_manual1.gif │ │ ├── 32_manual1.gif │ │ ├── 33_manual1.gif │ │ ├── 34_manual1.gif │ │ ├── 35_manual1.gif │ │ ├── 36_manual1.gif │ │ ├── 37_manual1.gif │ │ ├── 38_manual1.gif │ │ ├── 39_manual1.gif │ │ └── 40_manual1.gif │ │ ├── images │ │ ├── 21_training.tif │ │ ├── 22_training.tif │ │ ├── 23_training.tif │ │ ├── 24_training.tif │ │ ├── 25_training.tif │ │ ├── 26_training.tif │ │ ├── 27_training.tif │ │ ├── 28_training.tif │ │ ├── 29_training.tif │ │ ├── 30_training.tif │ │ ├── 31_training.tif │ │ ├── 32_training.tif │ │ ├── 33_training.tif │ │ ├── 34_training.tif │ │ ├── 35_training.tif │ │ ├── 36_training.tif │ │ ├── 37_training.tif │ │ ├── 38_training.tif │ │ ├── 39_training.tif │ │ └── 40_training.tif │ │ └── mask │ │ ├── 21_training_mask.gif │ │ ├── 22_training_mask.gif │ │ ├── 23_training_mask.gif │ │ ├── 24_training_mask.gif │ │ ├── 25_training_mask.gif │ │ ├── 26_training_mask.gif │ │ ├── 27_training_mask.gif │ │ ├── 28_training_mask.gif │ │ ├── 29_training_mask.gif │ │ ├── 30_training_mask.gif │ │ ├── 31_training_mask.gif │ │ ├── 32_training_mask.gif │ │ ├── 33_training_mask.gif │ │ ├── 34_training_mask.gif │ │ ├── 35_training_mask.gif │ │ ├── 36_training_mask.gif │ │ ├── 37_training_mask.gif │ │ ├── 38_training_mask.gif │ │ ├── 39_training_mask.gif │ │ └── 40_training_mask.gif ├── get_palette.py ├── my_dataset.py ├── palette.json ├── pascal_voc_classes.json ├── predict.py ├── requirements.txt ├── results20210918-122740.txt ├── results20221207-144657.txt ├── src │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── backbone.cpython-38.pyc │ │ └── fcn_model.cpython-38.pyc │ ├── backbone.py │ └── fcn_model.py ├── torch_fcn.png ├── train.py ├── train_multi_GPU.py ├── train_utils │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ ├── distributed_utils.cpython-38.pyc │ │ └── train_and_eval.cpython-38.pyc │ ├── distributed_utils.py │ └── train_and_eval.py ├── transforms.py └── validation.py ├── u2net ├── README.md ├── convert_weight.py ├── my_dataset.py ├── predict.py ├── requirements.txt ├── results20220723-123632.txt ├── src │ ├── __init__.py │ └── model.py ├── train.py ├── train_multi_GPU.py ├── train_utils │ ├── __init__.py │ ├── distributed_utils.py │ └── train_and_eval.py ├── transforms.py ├── u2net.png └── validation.py └── unet ├── .idea ├── .gitignore ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml ├── modules.xml └── unet.iml ├── DRIVE ├── test │ ├── 1st_manual │ │ ├── 01_manual1.gif │ │ ├── 02_manual1.gif │ │ ├── 03_manual1.gif │ │ ├── 04_manual1.gif │ │ ├── 05_manual1.gif │ │ ├── 06_manual1.gif │ │ ├── 07_manual1.gif │ │ ├── 08_manual1.gif │ │ ├── 09_manual1.gif │ │ ├── 10_manual1.gif │ │ ├── 11_manual1.gif │ │ ├── 12_manual1.gif │ │ ├── 13_manual1.gif │ │ ├── 14_manual1.gif │ │ ├── 15_manual1.gif │ │ ├── 16_manual1.gif │ │ ├── 17_manual1.gif │ │ ├── 18_manual1.gif │ │ ├── 19_manual1.gif │ │ └── 20_manual1.gif │ ├── 2nd_manual │ │ ├── 01_manual2.gif │ │ ├── 02_manual2.gif │ │ ├── 03_manual2.gif │ │ ├── 04_manual2.gif │ │ ├── 05_manual2.gif │ │ ├── 06_manual2.gif │ │ ├── 07_manual2.gif │ │ ├── 08_manual2.gif │ │ ├── 09_manual2.gif │ │ ├── 10_manual2.gif │ │ ├── 11_manual2.gif │ │ ├── 12_manual2.gif │ │ ├── 13_manual2.gif │ │ ├── 14_manual2.gif │ │ ├── 15_manual2.gif │ │ ├── 16_manual2.gif │ │ ├── 17_manual2.gif │ │ ├── 18_manual2.gif │ │ ├── 19_manual2.gif │ │ └── 20_manual2.gif │ ├── images │ │ ├── 01_test.tif │ │ ├── 02_test.tif │ │ ├── 03_test.tif │ │ ├── 04_test.tif │ │ ├── 05_test.tif │ │ ├── 06_test.tif │ │ ├── 07_test.tif │ │ ├── 08_test.tif │ │ ├── 09_test.tif │ │ ├── 10_test.tif │ │ ├── 11_test.tif │ │ ├── 12_test.tif │ │ ├── 13_test.tif │ │ ├── 14_test.tif │ │ ├── 15_test.tif │ │ ├── 16_test.tif │ │ ├── 17_test.tif │ │ ├── 18_test.tif │ │ ├── 19_test.tif │ │ └── 20_test.tif │ └── mask │ │ ├── 01_test_mask.gif │ │ ├── 02_test_mask.gif │ │ ├── 03_test_mask.gif │ │ ├── 04_test_mask.gif │ │ ├── 05_test_mask.gif │ │ ├── 06_test_mask.gif │ │ ├── 07_test_mask.gif │ │ ├── 08_test_mask.gif │ │ ├── 09_test_mask.gif │ │ ├── 10_test_mask.gif │ │ ├── 11_test_mask.gif │ │ ├── 12_test_mask.gif │ │ ├── 13_test_mask.gif │ │ ├── 14_test_mask.gif │ │ ├── 15_test_mask.gif │ │ ├── 16_test_mask.gif │ │ ├── 17_test_mask.gif │ │ ├── 18_test_mask.gif │ │ ├── 19_test_mask.gif │ │ └── 20_test_mask.gif └── training │ ├── 1st_manual │ ├── 21_manual1.gif │ ├── 22_manual1.gif │ ├── 23_manual1.gif │ ├── 24_manual1.gif │ ├── 25_manual1.gif │ ├── 26_manual1.gif │ ├── 27_manual1.gif │ ├── 28_manual1.gif │ ├── 29_manual1.gif │ ├── 30_manual1.gif │ ├── 31_manual1.gif │ ├── 32_manual1.gif │ ├── 33_manual1.gif │ ├── 34_manual1.gif │ ├── 35_manual1.gif │ ├── 36_manual1.gif │ ├── 37_manual1.gif │ ├── 38_manual1.gif │ ├── 39_manual1.gif │ └── 40_manual1.gif │ ├── images │ ├── 21_training.tif │ ├── 22_training.tif │ ├── 23_training.tif │ ├── 24_training.tif │ ├── 25_training.tif │ ├── 26_training.tif │ ├── 27_training.tif │ ├── 28_training.tif │ ├── 29_training.tif │ ├── 30_training.tif │ ├── 31_training.tif │ ├── 32_training.tif │ ├── 33_training.tif │ ├── 34_training.tif │ ├── 35_training.tif │ ├── 36_training.tif │ ├── 37_training.tif │ ├── 38_training.tif │ ├── 39_training.tif │ └── 40_training.tif │ └── mask │ ├── 21_training_mask.gif │ ├── 22_training_mask.gif │ ├── 23_training_mask.gif │ ├── 24_training_mask.gif │ ├── 25_training_mask.gif │ ├── 26_training_mask.gif │ ├── 27_training_mask.gif │ ├── 28_training_mask.gif │ ├── 29_training_mask.gif │ ├── 30_training_mask.gif │ ├── 31_training_mask.gif │ ├── 32_training_mask.gif │ ├── 33_training_mask.gif │ ├── 34_training_mask.gif │ ├── 35_training_mask.gif │ ├── 36_training_mask.gif │ ├── 37_training_mask.gif │ ├── 38_training_mask.gif │ ├── 39_training_mask.gif │ └── 40_training_mask.gif ├── README.md ├── __pycache__ ├── my_dataset.cpython-38.pyc └── transforms.cpython-38.pyc ├── compute_mean_std.py ├── my_dataset.py ├── predict.py ├── requirements.txt ├── results20220109-165837.txt ├── results20221022-151539.txt ├── results20221101-163228.txt ├── results20221207-141800.txt ├── results20230103-093030.txt ├── save_weights └── best_model.pth ├── src ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── mobilenet_unet.cpython-38.pyc │ ├── unet.cpython-38.pyc │ └── vgg_unet.cpython-38.pyc ├── mobilenet_unet.py ├── unet.py └── vgg_unet.py ├── test_result1.png ├── train.py ├── train_multi_GPU.py ├── train_utils ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── dice_coefficient_loss.cpython-38.pyc │ ├── distributed_utils.cpython-38.pyc │ └── train_and_eval.cpython-38.pyc ├── dice_coefficient_loss.py ├── distributed_utils.py └── train_and_eval.py ├── transforms.py └── unet.png /Inf-Net-master/Code/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/Code/__init__.py -------------------------------------------------------------------------------- /Inf-Net-master/Code/model_lung_infection/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/Code/model_lung_infection/__init__.py -------------------------------------------------------------------------------- /Inf-Net-master/Code/model_lung_infection/backbone/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/Code/model_lung_infection/backbone/__init__.py -------------------------------------------------------------------------------- /Inf-Net-master/Code/model_lung_infection/module/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/Code/model_lung_infection/module/__init__.py -------------------------------------------------------------------------------- /Inf-Net-master/Code/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/Code/utils/__init__.py -------------------------------------------------------------------------------- /Inf-Net-master/Code/utils/onehot.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def onehot(data, n): 5 | """onehot ecoder""" 6 | buf = np.zeros(data.shape + (n,)) 7 | nmsk = np.arange(data.size) * n + data.ravel() 8 | buf.ravel()[nmsk - 1] = 1 9 | return buf 10 | -------------------------------------------------------------------------------- /Inf-Net-master/Code/utils/split_1600.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | """Preview 4 | Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans' 5 | submit to Transactions on Medical Imaging, 2020. 6 | 7 | First Version: Created on 2020-05-13 (@author: Ge-Peng Ji) 8 | """ 9 | 10 | import os 11 | import shutil 12 | import random 13 | 14 | unlabeled_imgs_dir = '../../Dataset/TrainingSet/LungInfection-Train/Pseudo-label/Imgs' 15 | save_path = '../../Dataset/TrainingSet/LungInfection-Train/Pseudo-label/DataPrepare/Imgs_splits' 16 | 17 | img_list = os.listdir(unlabeled_imgs_dir) 18 | random.shuffle(img_list) 19 | 20 | print(img_list) 21 | 22 | for i in range(0, int(len(img_list)/5)): 23 | choosed_img_lits = img_list[i*5:i*5+5] 24 | for img_name in choosed_img_lits: 25 | save_split_path = os.path.join(save_path, 'image_{}'.format(i)) 26 | os.makedirs(save_split_path, exist_ok=True) 27 | shutil.copyfile(os.path.join(unlabeled_imgs_dir, img_name), 28 | os.path.join(save_split_path, img_name)) 29 | print('[Processed] image_{}: {}'.format(i, choosed_img_lits)) -------------------------------------------------------------------------------- /Inf-Net-master/Code/utils/split_class.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | """Preview 4 | Code for 'Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans' 5 | submit to Transactions on Medical Imaging, 2020. 6 | 7 | First Version: Created on 2020-05-13 (@author: Ge-Peng Ji) 8 | """ 9 | 10 | from PIL import Image 11 | import os 12 | import numpy as np 13 | 14 | 15 | def split_class(pred_path, img_name, w_gt, h_gt): 16 | im = Image.open(os.path.join(pred_path, img_name)).convert('L') 17 | im_array_red = np.array(im) # 0, 38 18 | im_array_green = np.array(im) # 0, 75 19 | print(np.unique(im_array_red)) 20 | 21 | im_array_red[im_array_red != 0] = 1 22 | im_array_red[im_array_red == 0] = 255 23 | im_array_red[im_array_red == 1] = 0 24 | 25 | im_array_green[im_array_green != 128] = 0 26 | im_array_green[im_array_green == 128] = 255 27 | 28 | cls_save_1 = pred_path.replace('class_12', 'Ground-glass opacities/Semi-Inf-Net_UNet') 29 | cls_save_2 = pred_path.replace('class_12', 'Consolidation/Semi-Inf-Net_UNet') 30 | os.makedirs(cls_save_1, exist_ok=True) 31 | os.makedirs(cls_save_2, exist_ok=True) 32 | 33 | Image.fromarray(im_array_red).convert('1').resize(size=(h_gt, w_gt)).save(os.path.join(cls_save_1, img_name)) 34 | Image.fromarray(im_array_green).convert('1').resize(size=(h_gt, w_gt)).save(os.path.join(cls_save_2, img_name)) -------------------------------------------------------------------------------- /Inf-Net-master/Docs/Using Inf-Net in Google Colab- A Debugging Guideline.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/Docs/Using Inf-Net in Google Colab- A Debugging Guideline.pdf -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/LungInfection-Test-1-mat/Inf-Net.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/LungInfection-Test-1-mat/Inf-Net.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/LungInfection-Test-1-mat/Semi-Inf-Net.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/LungInfection-Test-1-mat/Semi-Inf-Net.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/LungInfection-Test-1_result.txt: -------------------------------------------------------------------------------- 1 | (Dataset:LungInfection-Test; Model:Inf-Net) meanDic:0.682;meanSen:0.692;meanSpe:0.943;Sm:0.781;meanEm:0.838;MAE:0.082. 2 | (Dataset:LungInfection-Test; Model:Semi-Inf-Net) meanDic:0.739;meanSen:0.725;meanSpe:0.960;Sm:0.800;meanEm:0.894;MAE:0.064. 3 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/MultiClassInfection-Test-1-mat/Semi-Inf-Net_UNet.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/MultiClassInfection-Test-1-mat/Semi-Inf-Net_UNet.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/MultiClassInfection-Test-1_result.txt: -------------------------------------------------------------------------------- 1 | (Dataset:MultiClassInfection-Test; Model:Semi-Inf-Net_UNet) meanDic:0.624;meanSen:0.618;meanSpe:0.966;Sm:0.706;meanEm:0.889;MAE:0.067. 2 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/MultiClassInfection-Test-2-mat/Semi-Inf-Net_UNet.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/MultiClassInfection-Test-2-mat/Semi-Inf-Net_UNet.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/MultiClassInfection-Test-2_result.txt: -------------------------------------------------------------------------------- 1 | (Dataset:MultiClassInfection-Test; Model:Semi-Inf-Net_UNet) meanDic:0.362;meanSen:0.403;meanSpe:0.766;Sm:0.683;meanEm:0.813;MAE:0.039. 2 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/Inf-Net.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/Inf-Net.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/Semi-Inf-Net.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/Semi-Inf-Net.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/UNet++.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/UNet++.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/UNet.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1-mat/UNet.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/LungInfection-Test-1_result.txt: -------------------------------------------------------------------------------- 1 | (Dataset:LungInfection-Test; Model:UNet) adpDice/adpF1:0.532; adpEm:0.779; adpIoU:0.362; adpPre:0.525;adpRec/adpSen:0.624;adpSpe:0.924;meanDic:0.439;meanEm:0.625;meanFm:0.439;meanSen:0.534;meanSpe:0.858;maxDic:0.558;maxEm:0.802;maxFm:0.558;Sm:0.622;MAE:0.186. 2 | (Dataset:LungInfection-Test; Model:UNet++) adpDice/adpF1:0.631; adpEm:0.774; adpIoU:0.460; adpPre:0.573;adpRec/adpSen:0.822;adpSpe:0.916;meanDic:0.581;meanEm:0.720;meanFm:0.581;meanSen:0.672;meanSpe:0.902;maxDic:0.706;maxEm:0.893;maxFm:0.706;Sm:0.722;MAE:0.120. 3 | (Dataset:LungInfection-Test; Model:Inf-Net) adpDice/adpF1:0.727; adpEm:0.885; adpIoU:0.571; adpPre:0.689;adpRec/adpSen:0.808;adpSpe:0.953;meanDic:0.682;meanEm:0.838;meanFm:0.682;meanSen:0.692;meanSpe:0.943;maxDic:0.746;maxEm:0.916;maxFm:0.746;Sm:0.781;MAE:0.082. 4 | (Dataset:LungInfection-Test; Model:Semi-Inf-Net) adpDice/adpF1:0.764; adpEm:0.924; adpIoU:0.618; adpPre:0.747;adpRec/adpSen:0.797;adpSpe:0.963;meanDic:0.739;meanEm:0.894;meanFm:0.739;meanSen:0.725;meanSpe:0.960;maxDic:0.775;maxEm:0.934;maxFm:0.775;Sm:0.800;MAE:0.064. 5 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/DeepLabV3Plus_Stride16.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/DeepLabV3Plus_Stride16.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/DeepLabV3Plus_Stride8.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/DeepLabV3Plus_Stride8.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/FCN8s_1100.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/FCN8s_1100.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/Semi-Inf-Net_FCN8s_1100.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1-mat/Semi-Inf-Net_FCN8s_1100.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-1_result.txt: -------------------------------------------------------------------------------- 1 | (Dataset:MultiClassInfection-Test; Model:DeepLabV3Plus_Stride8) adpDice/adpF1:0.380; adpEm:0.668; adpIoU:0.235; adpPre:0.434;adpRec/adpSen:0.518;adpSpe:0.866;meanDic:0.375;meanEm:0.675;meanFm:0.375;meanSen:0.478;meanSpe:0.863;maxDic:0.376;maxEm:0.677;maxFm:0.376;Sm:0.544;MAE:0.123. 2 | (Dataset:MultiClassInfection-Test; Model:DeepLabV3Plus_Stride16) adpDice/adpF1:0.447; adpEm:0.636; adpIoU:0.288; adpPre:0.381;adpRec/adpSen:0.733;adpSpe:0.826;meanDic:0.443;meanEm:0.655;meanFm:0.443;meanSen:0.713;meanSpe:0.823;maxDic:0.444;maxEm:0.657;maxFm:0.444;Sm:0.548;MAE:0.156. 3 | (Dataset:MultiClassInfection-Test; Model:FCN8s_1100) adpDice/adpF1:0.473; adpEm:0.776; adpIoU:0.309; adpPre:0.501;adpRec/adpSen:0.556;adpSpe:0.908;meanDic:0.471;meanEm:0.774;meanFm:0.471;meanSen:0.537;meanSpe:0.905;maxDic:0.472;maxEm:0.776;maxFm:0.472;Sm:0.582;MAE:0.101. 4 | (Dataset:MultiClassInfection-Test; Model:Semi-Inf-Net_FCN8s_1100) adpDice/adpF1:0.648; adpEm:0.884; adpIoU:0.479; adpPre:0.618;adpRec/adpSen:0.719;adpSpe:0.944;meanDic:0.646;meanEm:0.882;meanFm:0.646;meanSen:0.720;meanSpe:0.941;maxDic:0.648;maxEm:0.884;maxFm:0.648;Sm:0.711;MAE:0.071. 5 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/DeepLabV3Plus_Stride16.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/DeepLabV3Plus_Stride16.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/DeepLabV3Plus_Stride8.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/DeepLabV3Plus_Stride8.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/FCN8s_1100.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/FCN8s_1100.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/Semi-Inf-Net_FCN8s_1100.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2-mat/Semi-Inf-Net_FCN8s_1100.mat -------------------------------------------------------------------------------- /Inf-Net-master/EvaluateResults/previous/MultiClassInfection-Test-2_result.txt: -------------------------------------------------------------------------------- 1 | (Dataset:MultiClassInfection-Test; Model:DeepLabV3Plus_Stride8) adpDice/adpF1:0.122; adpEm:0.622; adpIoU:0.065; adpPre:0.199;adpRec/adpSen:0.160;adpSpe:0.587;meanDic:0.117;meanEm:0.621;meanFm:0.117;meanSen:0.120;meanSpe:0.584;maxDic:0.117;maxEm:0.622;maxFm:0.117;Sm:0.603;MAE:0.052. 2 | (Dataset:MultiClassInfection-Test; Model:DeepLabV3Plus_Stride16) adpDice/adpF1:0.197; adpEm:0.689; adpIoU:0.109; adpPre:0.212;adpRec/adpSen:0.389;adpSpe:0.562;meanDic:0.188;meanEm:0.688;meanFm:0.188;meanSen:0.245;meanSpe:0.560;maxDic:0.189;maxEm:0.689;maxFm:0.189;Sm:0.617;MAE:0.061. 3 | (Dataset:MultiClassInfection-Test; Model:FCN8s_1100) adpDice/adpF1:0.229; adpEm:0.652; adpIoU:0.129; adpPre:0.410;adpRec/adpSen:0.418;adpSpe:0.569;meanDic:0.221;meanEm:0.651;meanFm:0.221;meanSen:0.212;meanSpe:0.567;maxDic:0.222;maxEm:0.652;maxFm:0.222;Sm:0.652;MAE:0.039. 4 | (Dataset:MultiClassInfection-Test; Model:Semi-Inf-Net_FCN8s_1100) adpDice/adpF1:0.242; adpEm:0.661; adpIoU:0.138; adpPre:0.471;adpRec/adpSen:0.288;adpSpe:0.642;meanDic:0.238;meanEm:0.660;meanFm:0.238;meanSen:0.186;meanSpe:0.639;maxDic:0.239;maxEm:0.661;maxFm:0.239;Sm:0.661;MAE:0.035. 5 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluationTool/CalMAE.m: -------------------------------------------------------------------------------- 1 | function mae = CalMAE(smap, gtImg) 2 | % Code Author: Wangjiang Zhu 3 | % Email: wangjiang88119@gmail.com 4 | % Date: 3/24/2014 5 | if size(smap, 1) ~= size(gtImg, 1) || size(smap, 2) ~= size(gtImg, 2) 6 | error('Saliency map and gt Image have different sizes!\n'); 7 | end 8 | 9 | if ~islogical(gtImg) 10 | gtImg = gtImg(:,:,1) > 128; 11 | end 12 | 13 | smap = im2double(smap(:,:,1)); 14 | fgPixels = smap(gtImg); 15 | fgErrSum = length(fgPixels) - sum(fgPixels); 16 | bgErrSum = sum(smap(~gtImg)); 17 | mae = (fgErrSum + bgErrSum) / numel(gtImg); -------------------------------------------------------------------------------- /Inf-Net-master/EvaluationTool/Fmeasure_calu.m: -------------------------------------------------------------------------------- 1 | %% 2 | function [PreFtem, RecallFtem, SpecifTem, Dice, FmeasureF] = Fmeasure_calu(sMap,gtMap,gtsize, threshold) 3 | %threshold = 2* mean(sMap(:)) ; 4 | if ( threshold > 1 ) 5 | threshold = 1; 6 | end 7 | 8 | Label3 = zeros( gtsize ); 9 | Label3( sMap>=threshold ) = 1; 10 | 11 | NumRec = length( find( Label3==1 ) ); %FP+TP 12 | NumNoRec = length(find(Label3==0)); % FN+TN 13 | LabelAnd = Label3 & gtMap; 14 | NumAnd = length( find ( LabelAnd==1 ) ); %TP 15 | num_obj = sum(sum(gtMap)); %TP+FN 16 | num_pred = sum(sum(Label3)); % FP+TP 17 | 18 | FN = num_obj-NumAnd; 19 | FP = NumRec-NumAnd; 20 | TN = NumNoRec-FN; 21 | %SpecifTem = TN/(TN+FP) 22 | %Precision = TP/(TP+FP) 23 | 24 | if NumAnd == 0 25 | PreFtem = 0; 26 | RecallFtem = 0; 27 | FmeasureF = 0; 28 | Dice = 0; 29 | SpecifTem = 0; 30 | else 31 | PreFtem = NumAnd/NumRec; 32 | RecallFtem = NumAnd/num_obj; 33 | SpecifTem = TN/(TN+FP); 34 | Dice = 2 * NumAnd/(num_obj+num_pred); 35 | % FmeasureF = ( ( 1.3* PreFtem * RecallFtem ) / ( .3 * PreFtem + RecallFtem ) ); % beta = 0.3 36 | FmeasureF = (( 2.0 * PreFtem * RecallFtem ) / (PreFtem + RecallFtem)); % beta = 1.0 37 | end 38 | 39 | %Fmeasure = [PreFtem, RecallFtem, FmeasureF]; 40 | 41 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluationTool/StructureMeasure.m: -------------------------------------------------------------------------------- 1 | function Q = StructureMeasure(prediction,GT) 2 | % StructureMeasure computes the similarity between the foreground map and 3 | % ground truth(as proposed in "Structure-measure: A new way to evaluate 4 | % foreground maps" [Deng-Ping Fan et. al - ICCV 2017]) 5 | % Usage: 6 | % Q = StructureMeasure(prediction,GT) 7 | % Input: 8 | % prediction - Binary/Non binary foreground map with values in the range 9 | % [0 1]. Type: double. 10 | % GT - Binary ground truth. Type: logical. 11 | % Output: 12 | % Q - The computed similarity score 13 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 14 | 15 | % Check input 16 | if (~isa(prediction,'double')) 17 | error('The prediction should be double type...'); 18 | end 19 | if ((max(prediction(:))>1) || min(prediction(:))<0) 20 | error('The prediction should be in the range of [0 1]...'); 21 | end 22 | if (~islogical(GT)) 23 | error('GT should be logical type...'); 24 | end 25 | 26 | y = mean2(GT); 27 | 28 | if (y==0)% if the GT is completely black 29 | x = mean2(prediction); 30 | Q = 1.0 - x; %only calculate the area of intersection 31 | elseif(y==1)%if the GT is completely white 32 | x = mean2(prediction); 33 | Q = x; %only calcualte the area of intersection 34 | else 35 | alpha = 0.5; 36 | Q = alpha*S_object(prediction,GT)+(1-alpha)*S_region(prediction,GT); 37 | if (Q<0) 38 | Q=0; 39 | end 40 | end 41 | 42 | end 43 | -------------------------------------------------------------------------------- /Inf-Net-master/EvaluationTool/original_WFb.m: -------------------------------------------------------------------------------- 1 | function [Q]= original_WFb(FG,GT) 2 | % WFb Compute the Weighted F-beta measure (as proposed in "How to Evaluate 3 | % Foreground Maps?" [Margolin et. al - CVPR'14]) 4 | % Usage: 5 | % Q = FbW(FG,GT) 6 | % Input: 7 | % FG - Binary/Non binary foreground map with values in the range [0 1]. Type: double. 8 | % GT - Binary ground truth. Type: logical. 9 | % Output: 10 | % Q - The Weighted F-beta score 11 | 12 | %Check input 13 | if (~isa( FG, 'double' )) 14 | error('FG should be of type: double'); 15 | end 16 | if ((max(FG(:))>1) || min(FG(:))<0) 17 | error('FG should be in the range of [0 1]'); 18 | end 19 | if (~islogical(GT)) 20 | error('GT should be of type: logical'); 21 | end 22 | 23 | dGT = double(GT); %Use double for computations. 24 | 25 | 26 | E = abs(FG-dGT); 27 | % [Ef, Et, Er] = deal(abs(FG-GT)); 28 | 29 | [Dst,IDXT] = bwdist(dGT); 30 | %Pixel dependency 31 | K = fspecial('gaussian',7,5); 32 | Et = E; 33 | Et(~GT)=Et(IDXT(~GT)); %To deal correctly with the edges of the foreground region 34 | EA = imfilter(Et,K); 35 | MIN_E_EA = E; 36 | MIN_E_EA(GT & EA 128; 11 | end 12 | 13 | smap = im2double(smap(:,:,1)); 14 | fgPixels = smap(gtImg); 15 | fgErrSum = length(fgPixels) - sum(fgPixels); 16 | bgErrSum = sum(smap(~gtImg)); 17 | mae = (fgErrSum + bgErrSum) / numel(gtImg); -------------------------------------------------------------------------------- /PraNet-master/eval/Fmeasure_calu.m: -------------------------------------------------------------------------------- 1 | %% 2 | function [PreFtem, RecallFtem, SpecifTem, Dice, FmeasureF, IoU] = Fmeasure_calu(sMap, gtMap, gtsize, threshold) 3 | %threshold = 2* mean(sMap(:)) ; 4 | if ( threshold > 1 ) 5 | threshold = 1; 6 | end 7 | 8 | Label3 = zeros( gtsize ); 9 | Label3( sMap>=threshold ) = 1; 10 | 11 | NumRec = length( find( Label3==1 ) ); %FP+TP 12 | NumNoRec = length(find(Label3==0)); % FN+TN 13 | LabelAnd = Label3 & gtMap; 14 | NumAnd = length( find ( LabelAnd==1 ) ); %TP 15 | num_obj = sum(sum(gtMap)); %TP+FN 16 | num_pred = sum(sum(Label3)); % FP+TP 17 | 18 | FN = num_obj-NumAnd; 19 | FP = NumRec-NumAnd; 20 | TN = NumNoRec-FN; 21 | 22 | %SpecifTem = TN/(TN+FP) 23 | %Precision = TP/(TP+FP) 24 | 25 | if NumAnd == 0 26 | PreFtem = 0; 27 | RecallFtem = 0; 28 | FmeasureF = 0; 29 | Dice = 0; 30 | SpecifTem = 0; 31 | IoU = 0; 32 | else 33 | IoU = NumAnd/(FN+NumRec); %TP/(FN+TP+FP) 34 | PreFtem = NumAnd/NumRec; 35 | RecallFtem = NumAnd/num_obj; 36 | SpecifTem = TN/(TN+FP); 37 | Dice = 2 * NumAnd/(num_obj+num_pred); 38 | % FmeasureF = ( ( 1.3* PreFtem * RecallFtem ) / ( .3 * PreFtem + RecallFtem ) ); % beta = 0.3 39 | FmeasureF = (( 2.0 * PreFtem * RecallFtem ) / (PreFtem + RecallFtem)); % beta = 1.0 40 | end 41 | 42 | %Fmeasure = [PreFtem, RecallFtem, FmeasureF]; 43 | 44 | -------------------------------------------------------------------------------- /PraNet-master/eval/README.md: -------------------------------------------------------------------------------- 1 | # Evaluation code for “PraNet: Parallel Reverse Attention Network for Polyp Segmentation” (MICCAI-2020) 2 | 3 | > Author: Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao 4 | 5 | Homepage: http://dpfan.net/ 6 | 7 | Project Page: https://github.com/DengPingFan/PraNet 8 | 9 | Version: 2020-6-29 10 | 11 | Any questions please contact with dengpfan@gmail.com 12 | 13 | If you find this project is useful, please cite our work. Thanks. 14 | 15 | Title: “PraNet: Parallel Reverse Attention Network for Polyp Segmentation” MICCAI 2020 16 | 17 | ## Usage 18 | 19 | - The Dataset folder contains the five different test datasets. 20 | 21 | - The results folder consists of several compared models. 22 | 23 | - You can just run the `main.m` in the EvaluationTool folder to get the final evaluation results in the EvaluateResults folder. 24 | -------------------------------------------------------------------------------- /PraNet-master/eval/StructureMeasure.m: -------------------------------------------------------------------------------- 1 | function Q = StructureMeasure(prediction,GT) 2 | % StructureMeasure computes the similarity between the foreground map and 3 | % ground truth(as proposed in "Structure-measure: A new way to evaluate 4 | % foreground maps" [Deng-Ping Fan et. al - ICCV 2017]) 5 | % Usage: 6 | % Q = StructureMeasure(prediction,GT) 7 | % Input: 8 | % prediction - Binary/Non binary foreground map with values in the range 9 | % [0 1]. Type: double. 10 | % GT - Binary ground truth. Type: logical. 11 | % Output: 12 | % Q - The computed similarity score 13 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 14 | 15 | % Check input 16 | if (~isa(prediction,'double')) 17 | error('The prediction should be double type...'); 18 | end 19 | if ((max(prediction(:))>1) || min(prediction(:))<0) 20 | error('The prediction should be in the range of [0 1]...'); 21 | end 22 | if (~islogical(GT)) 23 | error('GT should be logical type...'); 24 | end 25 | 26 | y = mean2(GT); 27 | 28 | if (y==0)% if the GT is completely black 29 | x = mean2(prediction); 30 | Q = 1.0 - x; %only calculate the area of intersection 31 | elseif(y==1)%if the GT is completely white 32 | x = mean2(prediction); 33 | Q = x; %only calcualte the area of intersection 34 | else 35 | alpha = 0.5; 36 | Q = alpha*S_object(prediction,GT)+(1-alpha)*S_region(prediction,GT); 37 | if (Q<0) 38 | Q=0; 39 | end 40 | end 41 | 42 | end 43 | -------------------------------------------------------------------------------- /PraNet-master/eval/original_WFb.m: -------------------------------------------------------------------------------- 1 | function [Q]= original_WFb(FG,GT) 2 | % WFb Compute the Weighted F-beta measure (as proposed in "How to Evaluate 3 | % Foreground Maps?" [Margolin et. al - CVPR'14]) 4 | % Usage: 5 | % Q = FbW(FG,GT) 6 | % Input: 7 | % FG - Binary/Non binary foreground map with values in the range [0 1]. Type: double. 8 | % GT - Binary ground truth. Type: logical. 9 | % Output: 10 | % Q - The Weighted F-beta score 11 | 12 | %Check input 13 | if (~isa( FG, 'double' )) 14 | error('FG should be of type: double'); 15 | end 16 | if ((max(FG(:))>1) || min(FG(:))<0) 17 | error('FG should be in the range of [0 1]'); 18 | end 19 | if (~islogical(GT)) 20 | error('GT should be of type: logical'); 21 | end 22 | 23 | dGT = double(GT); %Use double for computations. 24 | 25 | 26 | E = abs(FG-dGT); 27 | % [Ef, Et, Er] = deal(abs(FG-GT)); 28 | 29 | [Dst,IDXT] = bwdist(dGT); 30 | %Pixel dependency 31 | K = fspecial('gaussian',7,5); 32 | Et = E; 33 | Et(~GT)=Et(IDXT(~GT)); %To deal correctly with the edges of the foreground region 34 | EA = imfilter(Et,K); 35 | MIN_E_EA = E; 36 | MIN_E_EA(GT & EA {} - {}'.format(_data_name, name)) 41 | imageio.imwrite((save_path + name[0]), res) -------------------------------------------------------------------------------- /PraNet-master/utils/__pycache__/dataloader.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/PraNet-master/utils/__pycache__/dataloader.cpython-36.pyc -------------------------------------------------------------------------------- /PraNet-master/utils/__pycache__/utils.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/PraNet-master/utils/__pycache__/utils.cpython-36.pyc -------------------------------------------------------------------------------- /PraNet-master/utils/format_conversion.py: -------------------------------------------------------------------------------- 1 | import os 2 | import shutil 3 | from libtiff import TIFF # pip install libtiff 4 | from scipy import misc 5 | import random 6 | 7 | 8 | def tif2png(_src_path, _dst_path): 9 | """ 10 | Usage: 11 | formatting `tif/tiff` files to `jpg/png` files 12 | :param _src_path: 13 | :param _dst_path: 14 | :return: 15 | """ 16 | tif = TIFF.open(_src_path, mode='r') 17 | image = tif.read_image() 18 | misc.imsave(_dst_path, image) 19 | 20 | 21 | def data_split(src_list): 22 | """ 23 | Usage: 24 | randomly spliting dataset 25 | :param src_list: 26 | :return: 27 | """ 28 | counter_list = random.sample(range(0, len(src_list)), 550) 29 | 30 | return counter_list 31 | 32 | 33 | if __name__ == '__main__': 34 | src_dir = '../Dataset/train_dataset/CVC-EndoSceneStill/CVC-612/test_split/masks_tif' 35 | dst_dir = '../Dataset/train_dataset/CVC-EndoSceneStill/CVC-612/test_split/masks' 36 | 37 | os.makedirs(dst_dir, exist_ok=True) 38 | for img_name in os.listdir(src_dir): 39 | tif2png(os.path.join(src_dir, img_name), 40 | os.path.join(dst_dir, img_name.replace('.tif', '.png'))) 41 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 医学图像分割领域的一些论文的代码 2 | ### 主要包括： 3 | 1.基础的医学图像分割论文如：**FCN、Unet、Unet++、deeplab_v3、u2net**. 4 | 2.一些特定任务的论文如：**脑肿瘤分割TBraTS-main、息肉分割PraNet-master、COVID19肺部感染区域分割Inf-Net-master**. 5 | 3.可信的医学图像分割如：**UMIS-mian（EvidenceCap）**. 6 | 持续更新... 7 | -------------------------------------------------------------------------------- /TBraTS-main/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /TBraTS-main/.idea/TBraTS-main.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /TBraTS-main/.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 14 | -------------------------------------------------------------------------------- /TBraTS-main/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /TBraTS-main/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /TBraTS-main/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /TBraTS-main/checkpoint/nullfile: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /TBraTS-main/image/F1N.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/TBraTS-main/image/F1N.png -------------------------------------------------------------------------------- /TBraTS-main/image/Trust_E.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/TBraTS-main/image/Trust_E.gif -------------------------------------------------------------------------------- /TBraTS-main/image/nullfile: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /TBraTS-main/log/nullfile: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /TBraTS-main/models/lib/IntmdSequential.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | class IntermediateSequential(nn.Sequential): 5 | def __init__(self, *args, return_intermediate=True): 6 | super().__init__(*args) 7 | self.return_intermediate = return_intermediate 8 | 9 | def forward(self, input): 10 | if not self.return_intermediate: 11 | return super().forward(input) 12 | 13 | intermediate_outputs = {} 14 | output = input 15 | for name, module in self.named_children(): 16 | output = intermediate_outputs[name] = module(output) 17 | 18 | return output, intermediate_outputs 19 | 20 | -------------------------------------------------------------------------------- /TBraTS-main/models/lib/PositionalEncoding.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | class FixedPositionalEncoding(nn.Module): 5 | def __init__(self, embedding_dim, max_length=512): 6 | super(FixedPositionalEncoding, self).__init__() 7 | 8 | pe = torch.zeros(max_length, embedding_dim) 9 | position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1) 10 | div_term = torch.exp( 11 | torch.arange(0, embedding_dim, 2).float() 12 | * (-torch.log(torch.tensor(10000.0)) / embedding_dim) 13 | ) 14 | pe[:, 0::2] = torch.sin(position * div_term) 15 | pe[:, 1::2] = torch.cos(position * div_term) 16 | pe = pe.unsqueeze(0).transpose(0, 1) 17 | self.register_buffer('pe', pe) 18 | 19 | def forward(self, x): 20 | x = x + self.pe[: x.size(0), :] 21 | return x 22 | 23 | 24 | class LearnedPositionalEncoding(nn.Module): 25 | def __init__(self, max_position_embeddings, embedding_dim, seq_length): 26 | super(LearnedPositionalEncoding, self).__init__() 27 | 28 | self.position_embeddings = nn.Parameter(torch.zeros(1, 4096, 512)) #8x 29 | 30 | def forward(self, x, position_ids=None): 31 | 32 | position_embeddings = self.position_embeddings 33 | return x + position_embeddings 34 | -------------------------------------------------------------------------------- /TBraTS-main/models/lib/nullfile: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /TBraTS-main/plot.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import os 3 | def loss_plot(args,loss): 4 | num = args.end_epoch 5 | x = [i for i in range(num)] 6 | plot_save_path = r'results/plot/' 7 | if not os.path.exists(plot_save_path): 8 | os.makedirs(plot_save_path) 9 | save_loss = plot_save_path+str(args.model_name)+'_'+str(args.batch_size)+'_'+str(args.dataset)+'_'+str(args.end_epoch)+'_loss.jpg' 10 | plt.figure() 11 | plt.plot(x,loss,label='loss') 12 | plt.legend() 13 | plt.savefig(save_loss) 14 | 15 | def metrics_plot(arg,name,*args): 16 | num = arg.end_epoch 17 | names = name.split('&') 18 | metrics_value = args 19 | i=0 20 | x = [i for i in range(num)] 21 | plot_save_path = r'results/plot/' 22 | if not os.path.exists(plot_save_path): 23 | os.makedirs(plot_save_path) 24 | save_metrics = plot_save_path + str(arg.model_name) + '_' + str(arg.batch_size) + '_' + str(arg.dataset) + '_' + str(arg.end_epoch) + '_'+name+'.jpg' 25 | plt.figure() 26 | for l in metrics_value: 27 | plt.plot(x,l,label=str(names[i])) 28 | #plt.scatter(x,l,label=str(l)) 29 | i+=1 30 | plt.legend() 31 | plt.savefig(save_metrics) -------------------------------------------------------------------------------- /TBraTS-main/results/nullfile: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /UMIS-main/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /UMIS-main/.idea/UMIS-main.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /UMIS-main/.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 14 | -------------------------------------------------------------------------------- /UMIS-main/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /UMIS-main/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /UMIS-main/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/checkpoint/null: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/data/null: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/image/Moti-TMIS.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UMIS-main/UMIS-main/image/Moti-TMIS.png -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/image/NC_F1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UMIS-main/UMIS-main/image/NC_F1.png -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/image/brats_fA2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UMIS-main/UMIS-main/image/brats_fA2.png -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/image/null: -------------------------------------------------------------------------------- 1 | 。。。 2 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/log/null: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/models/lib/IntmdSequential.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | class IntermediateSequential(nn.Sequential): 5 | def __init__(self, *args, return_intermediate=True): 6 | super().__init__(*args) 7 | self.return_intermediate = return_intermediate 8 | 9 | def forward(self, input): 10 | if not self.return_intermediate: 11 | return super().forward(input) 12 | 13 | intermediate_outputs = {} 14 | output = input 15 | for name, module in self.named_children(): 16 | output = intermediate_outputs[name] = module(output) 17 | 18 | return output, intermediate_outputs 19 | 20 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/models/lib/null: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/models/lib/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.autograd import Variable 4 | import torch.nn.functional as F 5 | import matplotlib.pyplot as plt 6 | 7 | def truncated_normal_(tensor, mean=0, std=1): 8 | size = tensor.shape 9 | tmp = tensor.new_empty(size + (4,)).normal_() 10 | valid = (tmp < 2) & (tmp > -2) 11 | ind = valid.max(-1, keepdim=True)[1] 12 | tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1)) 13 | tensor.data.mul_(std).add_(mean) 14 | K =1 15 | 16 | def init_weights(m): 17 | if type(m) == nn.Conv3d or type(m) == nn.ConvTranspose3d: 18 | nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu') 19 | #nn.init.normal_(m.weight, std=0.001) 20 | #nn.init.normal_(m.bias, std=0.001) 21 | truncated_normal_(m.bias, mean=0, std=0.001) 22 | k=1 23 | 24 | def init_weights_orthogonal_normal(m): 25 | if type(m) == nn.Conv3d or type(m) == nn.ConvTranspose3d: 26 | nn.init.orthogonal_(m.weight) 27 | truncated_normal_(m.bias, mean=0, std=0.001) 28 | #nn.init.normal_(m.bias, std=0.001) 29 | 30 | def l2_regularisation(m): 31 | l2_reg = None 32 | 33 | for W in m.parameters(): 34 | if l2_reg is None: 35 | l2_reg = W.norm(2) 36 | else: 37 | l2_reg = l2_reg + W.norm(2) 38 | return l2_reg 39 | 40 | def save_mask_prediction_example(mask, pred, iter): 41 | plt.imshow(pred[0,:,:],cmap='Greys') 42 | plt.savefig('images/'+str(iter)+"_prediction.png") 43 | plt.imshow(mask[0,:,:],cmap='Greys') 44 | plt.savefig('images/'+str(iter)+"_mask.png") -------------------------------------------------------------------------------- /UMIS-main/UMIS-main/results/null: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/.DS_Store -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | print("\nPlease cite the following paper when using nnUNet:\nFabian Isensee, Paul F. Jäger, Simon A. A. Kohl, " 3 | "Jens Petersen, Klaus H. Maier-Hein \"Automated Design of Deep Learning Methods for Biomedical Image " 4 | "Segmentation\" arXiv preprint arXiv:1904.08128 (2020).") 5 | print("If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n") 6 | 7 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/configuration.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | default_num_threads = 8 if 'nnUNet_def_n_proc' not in os.environ else int(os.environ['nnUNet_def_n_proc']) 4 | RESAMPLING_SEPARATE_Z_ANISO_THRESHOLD = 3 # determines what threshold to use for resampling the low resolution axis 5 | # separately (with NN) -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/dataset_conversion/Task017_BeyondCranialVaultAbdominalOrganSegmentation.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from batchgenerators.utilities.file_and_folder_operations import * 16 | import shutil 17 | 18 | 19 | if __name__ == "__main__": 20 | indir = "/home/fabian/drives/datasets/results/nnUNet/test_sets/Task017_AbdominalOrganSegmentation/predicted_3d_cascade_fullres" 21 | outdir = "/home/fabian/drives/datasets/results/nnUNet/test_sets/Task017_AbdominalOrganSegmentation/ready_for_submission" 22 | files = subfiles(indir, suffix='nii.gz', prefix="img", join=False) 23 | maybe_mkdir_p(outdir) 24 | for f in files: 25 | outname = "label" + f[3:] 26 | shutil.copy(join(indir, f), join(outdir, outname)) 27 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/dataset_conversion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | 3 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/evaluation/model_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/experiment_planner_baseline_3DUNet_v23.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import \ 16 | ExperimentPlanner3D_v21 17 | from nnunet.paths import * 18 | 19 | 20 | class ExperimentPlanner3D_v23(ExperimentPlanner3D_v21): 21 | """ 22 | """ 23 | def __init__(self, folder_with_cropped_data, preprocessed_output_folder): 24 | super(ExperimentPlanner3D_v23, self).__init__(folder_with_cropped_data, preprocessed_output_folder) 25 | self.data_identifier = "nnUNetData_plans_v2.3" 26 | self.plans_fname = join(self.preprocessed_output_folder, 27 | "nnUNetPlansv2.3_plans_3D.pkl") 28 | self.preprocessor_name = "Preprocessor3DDifferentResampling" 29 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/normalization/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/patch_size/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/pooling_and_convs/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/readme.md: -------------------------------------------------------------------------------- 1 | These alternatives are not used in nnU-Net, but you can use them if you believe they might be better suited for you. 2 | I (Fabian) have not found them to be consistently superior. -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/experiment_planning/alternative_experiment_planning/target_spacing/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/change_batch_size.py: -------------------------------------------------------------------------------- 1 | from batchgenerators.utilities.file_and_folder_operations import * 2 | import numpy as np 3 | 4 | if __name__ == '__main__': 5 | input_file = '/home/fabian/data/nnUNet_preprocessed/Task004_Hippocampus/nnUNetPlansv2.1_plans_3D.pkl' 6 | output_file = '/home/fabian/data/nnUNet_preprocessed/Task004_Hippocampus/nnUNetPlansv2.1_LISA_plans_3D.pkl' 7 | a = load_pickle(input_file) 8 | a['plans_per_stage'][0]['batch_size'] = int(np.floor(6 / 9 * a['plans_per_stage'][0]['batch_size'])) 9 | save_pickle(a, output_file) -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/experiment_planning/old/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/experiment_planning/old/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/inference/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/inference/pretrained_models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/inference/pretrained_models/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/network_architecture/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/network_architecture/custom_modules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/network_architecture/custom_modules/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/network_architecture/custom_modules/helperModules.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from torch import nn 17 | 18 | 19 | class Identity(nn.Module): 20 | def __init__(self, *args, **kwargs): 21 | super().__init__() 22 | 23 | def forward(self, input): 24 | return input 25 | 26 | 27 | class MyGroupNorm(nn.GroupNorm): 28 | def __init__(self, num_channels, eps=1e-5, affine=True, num_groups=8): 29 | super(MyGroupNorm, self).__init__(num_groups, num_channels, eps, affine) 30 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/network_architecture/custom_modules/mish.py: -------------------------------------------------------------------------------- 1 | ############ 2 | # https://github.com/lessw2020/mish/blob/master/mish.py 3 | # This code was taken from the repo above and was not created by me (Fabian)! Full credit goes to the original authors 4 | ############ 5 | 6 | import torch 7 | 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | 11 | 12 | # Mish - "Mish: A Self Regularized Non-Monotonic Neural Activation Function" 13 | # https://arxiv.org/abs/1908.08681v1 14 | # implemented for PyTorch / FastAI by lessw2020 15 | # github: https://github.com/lessw2020/mish 16 | 17 | class Mish(nn.Module): 18 | def __init__(self): 19 | super().__init__() 20 | 21 | def forward(self, x): 22 | # inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!) 23 | return x * (torch.tanh(F.softplus(x))) 24 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/postprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/postprocessing/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/preprocessing/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/run/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/cascade_stuff/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/data_augmentation/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/dataloading/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/learning_rate/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/learning_rate/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/learning_rate/poly_lr.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9): 17 | return initial_lr * (1 - epoch / max_epochs)**exponent 18 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/loss_functions/TopK_loss.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import numpy as np 16 | import torch 17 | from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss 18 | 19 | 20 | class TopKLoss(RobustCrossEntropyLoss): 21 | """ 22 | Network has to have NO LINEARITY! 23 | """ 24 | def __init__(self, weight=None, ignore_index=-100, k=10): 25 | self.k = k 26 | super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False) 27 | 28 | def forward(self, inp, target): 29 | target = target[:, 0].long() 30 | res = super(TopKLoss, self).forward(inp, target) 31 | num_voxels = np.prod(res.shape, dtype=np.int64) 32 | res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False) 33 | return res.mean() 34 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/loss_functions/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/loss_functions/crossentropy.py: -------------------------------------------------------------------------------- 1 | from torch import nn, Tensor 2 | 3 | 4 | class RobustCrossEntropyLoss(nn.CrossEntropyLoss): 5 | """ 6 | this is just a compatibility layer because my target tensor is float and has an extra dimension 7 | """ 8 | def forward(self, input: Tensor, target: Tensor) -> Tensor: 9 | if len(target.shape) == len(input.shape): 10 | assert target.shape[1] == 1 11 | target = target[:, 0] 12 | return super().forward(input, target.long()) -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/BraTS_trainer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/BraTS_trainer/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNetTrainerV2_fp32.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_fp32(nnUNetTrainerV2): 20 | """ 21 | Info for Fabian: same as internal nnUNetTrainerV2_2 22 | """ 23 | 24 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 25 | unpack_data=True, deterministic=True, fp16=False): 26 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 27 | deterministic, False) 28 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/architectural_variants/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/architectural_variants/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/architectural_variants/nnUNetTrainerV2_NoNormalization_lr1en3.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_NoNormalization import \ 17 | nnUNetTrainerV2_NoNormalization 18 | 19 | 20 | class nnUNetTrainerV2_NoNormalization_lr1en3(nnUNetTrainerV2_NoNormalization): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.initial_lr = 1e-3 26 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/cascade/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/cascade/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/cascade/nnUNetTrainerV2CascadeFullRes_lowerLR.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes 17 | 18 | 19 | class nnUNetTrainerV2CascadeFullRes_lowerLR(nnUNetTrainerV2CascadeFullRes): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, previous_trainer="nnUNetTrainerV2", fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, 23 | batch_dice, stage, unpack_data, deterministic, 24 | previous_trainer, fp16) 25 | self.initial_lr = 1e-3 26 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/cascade/nnUNetTrainerV2CascadeFullRes_shorter.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes 17 | 18 | 19 | class nnUNetTrainerV2CascadeFullRes_shorter(nnUNetTrainerV2CascadeFullRes): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, previous_trainer="nnUNetTrainerV2", fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, 23 | batch_dice, stage, unpack_data, deterministic, 24 | previous_trainer, fp16) 25 | self.max_num_epochs = 500 26 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/cascade/nnUNetTrainerV2CascadeFullRes_shorter_lowerLR.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes 17 | 18 | 19 | class nnUNetTrainerV2CascadeFullRes_shorter_lowerLR(nnUNetTrainerV2CascadeFullRes): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, previous_trainer="nnUNetTrainerV2", fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, 23 | batch_dice, stage, unpack_data, deterministic, 24 | previous_trainer, fp16) 25 | self.max_num_epochs = 500 26 | self.initial_lr = 1e-3 27 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/copies/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/copies/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/data_augmentation/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/data_augmentation/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/data_augmentation/nnUNetTrainerV2_DA2.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_DA2(nnUNetTrainerV2): 20 | def setup_DA_params(self): 21 | super().setup_DA_params() 22 | 23 | self.data_aug_params["independent_scale_factor_for_each_axis"] = True 24 | 25 | if self.threeD: 26 | self.data_aug_params["rotation_p_per_axis"] = 0.5 27 | else: 28 | self.data_aug_params["rotation_p_per_axis"] = 1 29 | 30 | self.data_aug_params["do_additive_brightness"] = True 31 | 32 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/data_augmentation/nnUNetTrainerV2_independentScalePerAxis.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_independentScalePerAxis(nnUNetTrainerV2): 20 | def setup_DA_params(self): 21 | super().setup_DA_params() 22 | self.data_aug_params["independent_scale_factor_for_each_axis"] = True 23 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_ForceBD.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_ForceBD(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | batch_dice = True 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_ForceSD.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_ForceSD(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | batch_dice = False 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_CE.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss 15 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 16 | 17 | 18 | class nnUNetTrainerV2_Loss_CE(nnUNetTrainerV2): 19 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 20 | unpack_data=True, deterministic=True, fp16=False): 21 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 22 | deterministic, fp16) 23 | self.loss = RobustCrossEntropyLoss() 24 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_CEGDL.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import GDL_and_CE_loss 18 | 19 | 20 | class nnUNetTrainerV2_Loss_CEGDL(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.loss = GDL_and_CE_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, {}) 26 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/loss_function/nnUNetTrainerV2_Loss_DiceTopK10.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | from nnunet.training.loss_functions.dice_loss import DC_and_topk_loss 18 | 19 | 20 | class nnUNetTrainerV2_Loss_DiceTopK10(nnUNetTrainerV2): 21 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 22 | unpack_data=True, deterministic=True, fp16=False): 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | self.loss = DC_and_topk_loss({'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}, 26 | {'k': 10}) 27 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/miscellaneous/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/miscellaneous/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/nnUNetTrainerCE.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss 15 | from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer 16 | 17 | 18 | class nnUNetTrainerCE(nnUNetTrainer): 19 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 20 | unpack_data=True, deterministic=True, fp16=False): 21 | super(nnUNetTrainerCE, self).__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, 22 | unpack_data, deterministic, fp16) 23 | self.loss = RobustCrossEntropyLoss() 24 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | import torch 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | 19 | 20 | class nnUNetTrainerV2_Adam(nnUNetTrainerV2): 21 | 22 | def initialize_optimizer_and_scheduler(self): 23 | self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, amsgrad=True) 24 | self.lr_scheduler = None 25 | 26 | 27 | nnUNetTrainerV2_Adam_copy1 = nnUNetTrainerV2_Adam 28 | nnUNetTrainerV2_Adam_copy2 = nnUNetTrainerV2_Adam 29 | nnUNetTrainerV2_Adam_copy3 = nnUNetTrainerV2_Adam 30 | nnUNetTrainerV2_Adam_copy4 = nnUNetTrainerV2_Adam 31 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam_lr_3en4.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNet_variants.optimizer_and_lr.nnUNetTrainerV2_Adam import nnUNetTrainerV2_Adam 17 | 18 | 19 | class nnUNetTrainerV2_Adam_nnUNetTrainerlr(nnUNetTrainerV2_Adam): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 23 | deterministic, fp16) 24 | self.initial_lr = 3e-4 25 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_fp16.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 17 | 18 | 19 | class nnUNetTrainerV2_fp16(nnUNetTrainerV2): 20 | def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, 21 | unpack_data=True, deterministic=True, fp16=False): 22 | assert fp16, "This one only accepts fp16=True" 23 | super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, 24 | deterministic, fp16) 25 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum09.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_momentum09(nnUNetTrainerV2): 22 | def initialize_optimizer_and_scheduler(self): 23 | assert self.network is not None, "self.initialize_network must be called first" 24 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 25 | momentum=0.9, nesterov=True) 26 | self.lr_scheduler = None 27 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum095.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_momentum095(nnUNetTrainerV2): 22 | def initialize_optimizer_and_scheduler(self): 23 | assert self.network is not None, "self.initialize_network must be called first" 24 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 25 | momentum=0.95, nesterov=True) 26 | self.lr_scheduler = None 27 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum098.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | import torch 17 | 18 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 19 | 20 | 21 | class nnUNetTrainerV2_momentum098(nnUNetTrainerV2): 22 | def initialize_optimizer_and_scheduler(self): 23 | assert self.network is not None, "self.initialize_network must be called first" 24 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 25 | momentum=0.98, nesterov=True) 26 | self.lr_scheduler = None 27 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_momentum09in2D.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | import torch 17 | from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 18 | 19 | 20 | class nnUNetTrainerV2_momentum09in2D(nnUNetTrainerV2): 21 | def initialize_optimizer_and_scheduler(self): 22 | if self.threeD: 23 | momentum = 0.99 24 | else: 25 | momentum = 0.9 26 | assert self.network is not None, "self.initialize_network must be called first" 27 | self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, 28 | momentum=momentum, nesterov=True) 29 | self.lr_scheduler = None 30 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/profiling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/profiling/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/resampling/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/network_training/nnUNet_variants/resampling/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/training/optimizer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/UNetPLusPlus-master/nnunet/training/optimizer/__init__.py -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from . import * -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/file_endings.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | from batchgenerators.utilities.file_and_folder_operations import * 17 | 18 | 19 | def remove_trailing_slash(filename: str): 20 | while filename.endswith('/'): 21 | filename = filename[:-1] 22 | return filename 23 | 24 | 25 | def maybe_add_0000_to_all_niigz(folder): 26 | nii_gz = subfiles(folder, suffix='.nii.gz') 27 | for n in nii_gz: 28 | n = remove_trailing_slash(n) 29 | if not n.endswith('_0000.nii.gz'): 30 | os.rename(n, n[:-7] + '_0000.nii.gz') 31 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/nd_softmax.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | from torch import nn 17 | import torch.nn.functional as F 18 | 19 | 20 | softmax_helper = lambda x: F.softmax(x, 1) 21 | 22 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/one_hot_encoding.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import numpy as np 16 | 17 | 18 | def to_one_hot(seg, all_seg_labels=None): 19 | if all_seg_labels is None: 20 | all_seg_labels = np.unique(seg) 21 | result = np.zeros((len(all_seg_labels), *seg.shape), dtype=seg.dtype) 22 | for i, l in enumerate(all_seg_labels): 23 | result[i][seg == l] = 1 24 | return result 25 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/random_stuff.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | class no_op(object): 17 | def __enter__(self): 18 | pass 19 | 20 | def __exit__(self, *args): 21 | pass 22 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/sitk_stuff.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | 16 | import SimpleITK as sitk 17 | 18 | 19 | def copy_geometry(image: sitk.Image, ref: sitk.Image): 20 | image.SetOrigin(ref.GetOrigin()) 21 | image.SetDirection(ref.GetDirection()) 22 | image.SetSpacing(ref.GetSpacing()) 23 | return image 24 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/nnunet/utilities/to_torch.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import torch 16 | 17 | 18 | def maybe_to_torch(d): 19 | if isinstance(d, list): 20 | d = [maybe_to_torch(i) if not isinstance(i, torch.Tensor) else i for i in d] 21 | elif not isinstance(d, torch.Tensor): 22 | d = torch.from_numpy(d).float() 23 | return d 24 | 25 | 26 | def to_cuda(data, non_blocking=True, gpu_id=0): 27 | if isinstance(data, list): 28 | data = [i.cuda(gpu_id, non_blocking=non_blocking) for i in data] 29 | else: 30 | data = data.cuda(gpu_id, non_blocking=True) 31 | return data 32 | -------------------------------------------------------------------------------- /UNetPLusPlus-master/setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = readme.md -------------------------------------------------------------------------------- /deeplab_v3/deeplabv3_resnet50.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/deeplab_v3/deeplabv3_resnet50.png -------------------------------------------------------------------------------- /deeplab_v3/get_palette.py: -------------------------------------------------------------------------------- 1 | import json 2 | import numpy as np 3 | from PIL import Image 4 | 5 | # 读取mask标签 6 | target = Image.open("./2007_001288.png") 7 | # 获取调色板 8 | palette = target.getpalette() 9 | palette = np.reshape(palette, (-1, 3)).tolist() 10 | # 转换成字典子形式 11 | pd = dict((i, color) for i, color in enumerate(palette)) 12 | 13 | json_str = json.dumps(pd) 14 | with open("palette.json", "w") as f: 15 | f.write(json_str) 16 | 17 | # target = np.array(target) 18 | # print(target) 19 | -------------------------------------------------------------------------------- /deeplab_v3/pascal_voc_classes.json: -------------------------------------------------------------------------------- 1 | { 2 | "aeroplane": 1, 3 | "bicycle": 2, 4 | "bird": 3, 5 | "boat": 4, 6 | "bottle": 5, 7 | "bus": 6, 8 | "car": 7, 9 | "cat": 8, 10 | "chair": 9, 11 | "cow": 10, 12 | "diningtable": 11, 13 | "dog": 12, 14 | "horse": 13, 15 | "motorbike": 14, 16 | "person": 15, 17 | "pottedplant": 16, 18 | "sheep": 17, 19 | "sofa": 18, 20 | "train": 19, 21 | "tvmonitor": 20 22 | } -------------------------------------------------------------------------------- /deeplab_v3/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.22.0 2 | torch==1.10.0 3 | torchvision==0.11.1 4 | Pillow 5 | -------------------------------------------------------------------------------- /deeplab_v3/src/__init__.py: -------------------------------------------------------------------------------- 1 | from .deeplabv3_model import deeplabv3_resnet50, deeplabv3_resnet101, deeplabv3_mobilenetv3_large 2 | -------------------------------------------------------------------------------- /deeplab_v3/train_utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .train_and_eval import train_one_epoch, evaluate, create_lr_scheduler 2 | from .distributed_utils import init_distributed_mode, save_on_master, mkdir 3 | -------------------------------------------------------------------------------- /fcn/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /fcn/.idea/fcn.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /fcn/.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 14 | -------------------------------------------------------------------------------- /fcn/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /fcn/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /fcn/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /fcn/__pycache__/my_dataset.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/__pycache__/my_dataset.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/__pycache__/transforms.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/__pycache__/transforms.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/01_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/01_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/02_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/02_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/03_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/03_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/04_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/04_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/05_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/05_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/06_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/06_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/07_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/07_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/08_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/08_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/09_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/09_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/10_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/10_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/11_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/11_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/12_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/12_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/13_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/13_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/14_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/14_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/15_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/15_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/16_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/16_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/17_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/17_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/18_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/18_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/19_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/19_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/1st_manual/20_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/1st_manual/20_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/01_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/01_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/02_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/02_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/03_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/03_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/04_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/04_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/05_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/05_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/06_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/06_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/07_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/07_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/08_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/08_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/09_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/09_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/10_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/10_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/11_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/11_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/12_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/12_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/13_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/13_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/14_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/14_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/15_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/15_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/16_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/16_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/17_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/17_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/18_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/18_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/19_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/19_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/2nd_manual/20_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/2nd_manual/20_manual2.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/01_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/01_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/02_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/02_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/03_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/03_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/04_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/04_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/05_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/05_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/06_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/06_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/07_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/07_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/08_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/08_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/09_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/09_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/10_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/10_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/11_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/11_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/12_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/12_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/13_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/13_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/14_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/14_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/15_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/15_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/16_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/16_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/17_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/17_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/18_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/18_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/19_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/19_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/images/20_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/images/20_test.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/01_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/01_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/02_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/02_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/03_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/03_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/04_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/04_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/05_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/05_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/06_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/06_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/07_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/07_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/08_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/08_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/09_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/09_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/10_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/10_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/11_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/11_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/12_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/12_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/13_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/13_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/14_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/14_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/15_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/15_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/16_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/16_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/17_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/17_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/18_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/18_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/19_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/19_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/test/mask/20_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/test/mask/20_test_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/21_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/21_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/22_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/22_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/23_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/23_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/24_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/24_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/25_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/25_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/26_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/26_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/27_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/27_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/28_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/28_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/29_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/29_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/30_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/30_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/31_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/31_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/32_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/32_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/33_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/33_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/34_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/34_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/35_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/35_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/36_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/36_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/37_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/37_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/38_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/38_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/39_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/39_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/1st_manual/40_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/1st_manual/40_manual1.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/21_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/21_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/22_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/22_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/23_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/23_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/24_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/24_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/25_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/25_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/26_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/26_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/27_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/27_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/28_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/28_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/29_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/29_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/30_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/30_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/31_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/31_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/32_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/32_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/33_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/33_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/34_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/34_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/35_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/35_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/36_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/36_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/37_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/37_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/38_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/38_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/39_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/39_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/images/40_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/images/40_training.tif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/21_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/21_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/22_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/22_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/23_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/23_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/24_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/24_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/25_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/25_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/26_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/26_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/27_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/27_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/28_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/28_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/29_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/29_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/30_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/30_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/31_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/31_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/32_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/32_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/33_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/33_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/34_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/34_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/35_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/35_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/36_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/36_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/37_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/37_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/38_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/38_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/39_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/39_training_mask.gif -------------------------------------------------------------------------------- /fcn/data/DRIVE/training/mask/40_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/data/DRIVE/training/mask/40_training_mask.gif -------------------------------------------------------------------------------- /fcn/get_palette.py: -------------------------------------------------------------------------------- 1 | import json 2 | import numpy as np 3 | from PIL import Image 4 | 5 | # 读取mask标签 6 | target = Image.open("./2007_001288.png") 7 | # 获取调色板 8 | palette = target.getpalette() 9 | palette = np.reshape(palette, (-1, 3)).tolist() 10 | # 转换成字典子形式 11 | pd = dict((i, color) for i, color in enumerate(palette)) 12 | 13 | json_str = json.dumps(pd) 14 | with open("palette.json", "w") as f: 15 | f.write(json_str) 16 | 17 | # target = np.array(target) 18 | # print(target) 19 | -------------------------------------------------------------------------------- /fcn/pascal_voc_classes.json: -------------------------------------------------------------------------------- 1 | { 2 | "aeroplane": 1, 3 | "bicycle": 2, 4 | "bird": 3, 5 | "boat": 4, 6 | "bottle": 5, 7 | "bus": 6, 8 | "car": 7, 9 | "cat": 8, 10 | "chair": 9, 11 | "cow": 10, 12 | "diningtable": 11, 13 | "dog": 12, 14 | "horse": 13, 15 | "motorbike": 14, 16 | "person": 15, 17 | "pottedplant": 16, 18 | "sheep": 17, 19 | "sofa": 18, 20 | "train": 19, 21 | "tvmonitor": 20 22 | } -------------------------------------------------------------------------------- /fcn/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.22.0 2 | torch==1.10.0 3 | torchvision==0.11.1 4 | Pillow 5 | -------------------------------------------------------------------------------- /fcn/src/__init__.py: -------------------------------------------------------------------------------- 1 | from .fcn_model import fcn_resnet50, fcn_resnet101 2 | -------------------------------------------------------------------------------- /fcn/src/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/src/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/src/__pycache__/backbone.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/src/__pycache__/backbone.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/src/__pycache__/fcn_model.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/src/__pycache__/fcn_model.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/torch_fcn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/torch_fcn.png -------------------------------------------------------------------------------- /fcn/train_utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .train_and_eval import train_one_epoch, evaluate, create_lr_scheduler 2 | from .distributed_utils import init_distributed_mode, save_on_master, mkdir 3 | -------------------------------------------------------------------------------- /fcn/train_utils/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/train_utils/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/train_utils/__pycache__/distributed_utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/train_utils/__pycache__/distributed_utils.cpython-38.pyc -------------------------------------------------------------------------------- /fcn/train_utils/__pycache__/train_and_eval.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/fcn/train_utils/__pycache__/train_and_eval.cpython-38.pyc -------------------------------------------------------------------------------- /u2net/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.22.0 2 | torch==1.10.0 3 | torchvision==0.11.1 4 | opencv_python==4.5.4.60 5 | -------------------------------------------------------------------------------- /u2net/src/__init__.py: -------------------------------------------------------------------------------- 1 | from .model import u2net_full, u2net_lite 2 | -------------------------------------------------------------------------------- /u2net/train_utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .train_and_eval import train_one_epoch, evaluate, create_lr_scheduler, get_params_groups 2 | from .distributed_utils import init_distributed_mode, save_on_master, mkdir 3 | -------------------------------------------------------------------------------- /u2net/u2net.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/u2net/u2net.png -------------------------------------------------------------------------------- /unet/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /unet/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /unet/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /unet/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /unet/.idea/unet.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/01_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/01_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/02_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/02_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/03_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/03_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/04_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/04_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/05_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/05_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/06_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/06_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/07_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/07_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/08_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/08_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/09_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/09_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/10_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/10_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/11_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/11_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/12_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/12_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/13_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/13_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/14_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/14_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/15_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/15_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/16_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/16_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/17_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/17_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/18_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/18_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/19_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/19_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/1st_manual/20_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/1st_manual/20_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/01_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/01_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/02_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/02_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/03_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/03_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/04_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/04_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/05_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/05_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/06_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/06_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/07_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/07_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/08_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/08_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/09_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/09_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/10_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/10_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/11_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/11_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/12_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/12_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/13_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/13_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/14_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/14_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/15_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/15_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/16_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/16_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/17_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/17_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/18_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/18_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/19_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/19_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/2nd_manual/20_manual2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/2nd_manual/20_manual2.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/01_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/01_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/02_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/02_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/03_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/03_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/04_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/04_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/05_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/05_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/06_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/06_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/07_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/07_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/08_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/08_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/09_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/09_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/10_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/10_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/11_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/11_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/12_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/12_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/13_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/13_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/14_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/14_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/15_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/15_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/16_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/16_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/17_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/17_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/18_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/18_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/19_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/19_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/images/20_test.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/images/20_test.tif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/01_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/01_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/02_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/02_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/03_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/03_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/04_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/04_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/05_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/05_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/06_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/06_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/07_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/07_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/08_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/08_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/09_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/09_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/10_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/10_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/11_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/11_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/12_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/12_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/13_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/13_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/14_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/14_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/15_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/15_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/16_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/16_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/17_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/17_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/18_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/18_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/19_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/19_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/test/mask/20_test_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/test/mask/20_test_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/21_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/21_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/22_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/22_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/23_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/23_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/24_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/24_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/25_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/25_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/26_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/26_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/27_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/27_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/28_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/28_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/29_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/29_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/30_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/30_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/31_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/31_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/32_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/32_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/33_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/33_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/34_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/34_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/35_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/35_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/36_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/36_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/37_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/37_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/38_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/38_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/39_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/39_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/1st_manual/40_manual1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/1st_manual/40_manual1.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/21_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/21_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/22_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/22_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/23_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/23_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/24_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/24_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/25_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/25_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/26_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/26_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/27_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/27_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/28_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/28_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/29_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/29_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/30_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/30_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/31_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/31_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/32_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/32_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/33_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/33_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/34_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/34_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/35_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/35_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/36_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/36_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/37_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/37_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/38_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/38_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/39_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/39_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/images/40_training.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/images/40_training.tif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/21_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/21_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/22_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/22_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/23_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/23_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/24_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/24_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/25_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/25_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/26_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/26_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/27_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/27_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/28_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/28_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/29_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/29_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/30_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/30_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/31_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/31_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/32_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/32_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/33_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/33_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/34_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/34_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/35_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/35_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/36_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/36_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/37_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/37_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/38_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/38_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/39_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/39_training_mask.gif -------------------------------------------------------------------------------- /unet/DRIVE/training/mask/40_training_mask.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/DRIVE/training/mask/40_training_mask.gif -------------------------------------------------------------------------------- /unet/__pycache__/my_dataset.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/__pycache__/my_dataset.cpython-38.pyc -------------------------------------------------------------------------------- /unet/__pycache__/transforms.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/__pycache__/transforms.cpython-38.pyc -------------------------------------------------------------------------------- /unet/compute_mean_std.py: -------------------------------------------------------------------------------- 1 | import os 2 | from PIL import Image 3 | import numpy as np 4 | 5 | 6 | def main(): 7 | img_channels = 3 8 | img_dir = "./DRIVE/training/images" 9 | roi_dir = "./DRIVE/training/mask" 10 | assert os.path.exists(img_dir), f"image dir: '{img_dir}' does not exist." 11 | assert os.path.exists(roi_dir), f"roi dir: '{roi_dir}' does not exist." 12 | 13 | img_name_list = [i for i in os.listdir(img_dir) if i.endswith(".tif")] 14 | cumulative_mean = np.zeros(img_channels) 15 | cumulative_std = np.zeros(img_channels) 16 | for img_name in img_name_list: 17 | img_path = os.path.join(img_dir, img_name) 18 | ori_path = os.path.join(roi_dir, img_name.replace(".tif", "_mask.gif")) 19 | img = np.array(Image.open(img_path)) / 255. 20 | roi_img = np.array(Image.open(ori_path).convert('L')) 21 | 22 | img = img[roi_img == 255] 23 | cumulative_mean += img.mean(axis=0) 24 | cumulative_std += img.std(axis=0) 25 | 26 | mean = cumulative_mean / len(img_name_list) 27 | std = cumulative_std / len(img_name_list) 28 | print(f"mean: {mean}") 29 | print(f"std: {std}") 30 | 31 | 32 | if __name__ == '__main__': 33 | main() 34 | -------------------------------------------------------------------------------- /unet/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.22.0 2 | torch==1.10.0 3 | torchvision==0.11.1 4 | Pillow 5 | -------------------------------------------------------------------------------- /unet/results20221101-163228.txt: -------------------------------------------------------------------------------- 1 | [epoch: 0] 2 | train_loss: 1.2330 3 | lr: 0.010000 4 | dice coefficient: 0.000 5 | global correct: 87.2 6 | average row correct: ['99.9', '0.0'] 7 | IoU: ['87.2', '0.0'] 8 | mean IoU: 43.6 9 | 10 | [epoch: 1] 11 | train_loss: 1.0624 12 | lr: 0.009909 13 | dice coefficient: 0.000 14 | global correct: 87.2 15 | average row correct: ['100.0', '0.0'] 16 | IoU: ['87.2', '0.0'] 17 | mean IoU: 43.6 18 | 19 | [epoch: 2] 20 | train_loss: 0.9775 21 | lr: 0.009818 22 | dice coefficient: 0.000 23 | global correct: 87.2 24 | average row correct: ['99.9', '0.0'] 25 | IoU: ['87.2', '0.0'] 26 | mean IoU: 43.6 27 | 28 | -------------------------------------------------------------------------------- /unet/save_weights/best_model.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/save_weights/best_model.pth -------------------------------------------------------------------------------- /unet/src/__init__.py: -------------------------------------------------------------------------------- 1 | from .unet import UNet 2 | from .mobilenet_unet import MobileV3Unet 3 | from .vgg_unet import VGG16UNet 4 | -------------------------------------------------------------------------------- /unet/src/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/src/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /unet/src/__pycache__/mobilenet_unet.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/src/__pycache__/mobilenet_unet.cpython-38.pyc -------------------------------------------------------------------------------- /unet/src/__pycache__/unet.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/src/__pycache__/unet.cpython-38.pyc -------------------------------------------------------------------------------- /unet/src/__pycache__/vgg_unet.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/src/__pycache__/vgg_unet.cpython-38.pyc -------------------------------------------------------------------------------- /unet/test_result1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/test_result1.png -------------------------------------------------------------------------------- /unet/train_utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .train_and_eval import train_one_epoch, evaluate, create_lr_scheduler 2 | from .distributed_utils import init_distributed_mode, save_on_master, mkdir 3 | -------------------------------------------------------------------------------- /unet/train_utils/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/train_utils/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /unet/train_utils/__pycache__/dice_coefficient_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/train_utils/__pycache__/dice_coefficient_loss.cpython-38.pyc -------------------------------------------------------------------------------- /unet/train_utils/__pycache__/distributed_utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/train_utils/__pycache__/distributed_utils.cpython-38.pyc -------------------------------------------------------------------------------- /unet/train_utils/__pycache__/train_and_eval.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/train_utils/__pycache__/train_and_eval.cpython-38.pyc -------------------------------------------------------------------------------- /unet/unet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangruohan1/MIS-Papercode-Dataset/404fbbd048465a5e9e96529a88dca3ca8e02fd62/unet/unet.png --------------------------------------------------------------------------------