└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # 组内资料分享 2 | 3 | - [组内资料分享](#组内资料分享) 4 | - [1. Paper-Reading-Group (论文分享)](#Paper-Reading-Group) 5 | - [2. MICS-Lecture-Share (MICS在线学术讲座)](#MICS-Lecture-Share) 6 | 7 | ## Paper-Reading-Group 8 | 9 | List shared papers in our group 10 | 11 | |Date|Speaker|Paper source|Title|Code| 12 | |---|:---:|:--|---|:-:| 13 | |2024.08.12|刘鑫雅
罗子豪|CVPR 2024|[Open-Set Domain Adaptation for Semantic Segmentation](https://openaccess.thecvf.com/content/CVPR2024/papers/Choe_Open-Set_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2024_paper.pdf)|[Pytorch](https://github.com/KHU-AGI/BUS)| 14 | |2024.08.05|王李廷煜
李赫|CVPR 2024|[Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation](https://openaccess.thecvf.com/content/CVPR2024/papers/Chi_Adaptive_Bidirectional_Displacement_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2024_paper.pdf)|[Pytorch](https://github.com/chy-upc/ABD)| 15 | |2024.07.29|伍江浩
周渝博|CVPR 2024|[Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation](https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Continual-MAE_Adaptive_Distribution_Masked_Autoencoders_for_Continual_Test-Time_Adaptation_CVPR_2024_paper.pdf)|| 16 | |2024.07.22|付佳
魏洁|CVPR 2024|[From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation](https://openaccess.thecvf.com/content/CVPR2024/papers/Kweon_From_SAM_to_CAMs_Exploring_Segment_Anything_Model_for_Weakly_CVPR_2024_paper.pdf)|[Pytorch](https://github.com/sangrockEG/S2C)| 17 | |2024.07.15|王李廷煜
罗子豪|EMNLP 2023|[RWKV: Reinventing RNNs for the Transformer Era](https://openreview.net/forum?id=7SaXczaBpG)|[Pytorch](https://github.com/BlinkDL/RWKV-LM)| 18 | |2024.07.08|韩梦
王旭晴|arXiv|[MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection](https://arxiv.org/abs/2404.06564)|[Pytorch](https://github.com/lewandofskee/MambaAD)| 19 | |2024.07.01|刘鑫雅
李赫|arXiv|[KAN: Kolmogorov–Arnold Networks](https://doi.org/10.48550/arXiv.2404.19756)|[Pytorch](https://github.com/KindXiaoming/pykan)| 20 | |2024.06.24|伍江浩
周渝博|CVPR 2024|[Vim4Path: Self-Supervised Vision Mamba for Histopathology Images](https://openaccess.thecvf.com/content/CVPR2024W/CVMI/papers/Nasiri-Sarvi_Vim4Path_Self-Supervised_Vision_Mamba_for_Histopathology_Images_CVPRW_2024_paper.pdf)|[Pytorch](https://github.com/AtlasAnalyticsLab/Vim4Path)| 21 | |2024.06.17|罗祥德
叶平|arXiv|[Demystify Mamba in Vision: A Linear Attention Perspective](https://arxiv.org/pdf/2405.16605)|[Pytorch](https://github.com/LeapLabTHU/MLLA)| 22 | |2024.06.03|钟岚烽
周先豪|arXiv|[Vision mamba: Efficient visual representation learning with bidirectional state space model](https://arxiv.org/pdf/2401.09417v2)|[Pytorch](https://github.com/hustvl/Vim)| 23 | |2024.05.27|魏洁
付佳|arXiv|[Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)|[Pytorch](https://github.com/state-spaces/mamba)| 24 | |2024.05.20|王李廷煜
罗子豪|Nature Medicine 2024|[Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging](https://www.nature.com/articles/s41591-024-02971-2)|[Pytorch](https://github.com/MedAI-Vision/CMR-AI)| 25 | |2024.05.13|罗祥德
李赫|Nature Machine Intelligence 2024|[Foundation model for cancer imaging biomarkers](https://www.nature.com/articles/s42256-024-00807-9)|[Pytorch](https://github.com/AIM-Harvard/foundation-cancer-image-biomarker)| 26 | |2024.04.29|伍江浩
刘鑫雅|Nature Medicine 2024|[Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning](https://www.nature.com/articles/s41591-024-02915-w)|[Pytorch](https://github.com/deeplearningplus/TORCH)| 27 | |2024.04.22|钟岚烽
邹宝胜|Nature Medicine 2024|[Towards a General-Purpose Foundation Model for Computational Pathology](https://www.nature.com/articles/s41591-024-02857-3)|[Pytorch](https://github.com/mahmoodlab/UNI)| 28 | |2024.04.15|韩梦
卢江山|arXiv|[MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation](https://arxiv.org/abs/2403.20253)| | 29 | |2024.04.08|周渝博
伍江浩|CVPR 2024|[One-Prompt to Segment All Medical Images](https://arxiv.org/abs/2305.10300)|[Pytorch](https://github.com/KidsWithTokens/one-prompt)| 30 | |2024.04.01|王李廷煜
曲义杰|arXiv|[Concatenate, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation](https://arxiv.org/html/2403.11229v1)|[Not available](https://github.com/ShumengLI/CFR)| 31 | |2024.03.25|付佳
魏洁|arXiv|[Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://arxiv.org/abs/2304.12620)|[Pytorch](https://github.com/KidsWithTokens/Medical-SAM-Adapter)| 32 | |2024.03.18|叶平
曲义杰|arXiv|[EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://arxiv.org/abs/2312.00863)|[Pytorch](https://github.com/yformer/EfficientSAM)| 33 | |2024.03.11|李赫
刘鑫雅|MIA 2024|[Segment anything model for medical images?](https://www.sciencedirect.com/science/article/abs/pii/S1361841523003213)|[Pytorch](https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images)| 34 | |2024.03.04|钟岚烽
邹宝胜|CVPR2023|[Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Visual_Language_Pretrained_Multiple_Instance_Zero-Shot_Transfer_for_Histopathology_Images_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/mahmoodlab/MI-Zero)| 35 | |2024.01.15|韩梦
卢江山|TMI 2023|[Hybrid Graph Convolutional Network With Online Masked Autoencoder for Robust Multimodal Cancer Survival Prediction](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10061470&tag=1)|[Pytorch](https://github.com/lin-lcx/HGCN)| 36 | |2024.01.08|叶平
周渝博|TMI 2024|[Federated Semi-supervised Medical Image Segmentation via Prototype-based Pseudo-labeling and Contrastive Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10250843)|[Pytorch](https://github.com/zhangbaiming/FedSemiSeg)| 37 | |2024.01.02|付佳
魏洁|CVPR 2023|[Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/minglllli/CBAFed)| 38 | |2023.12.25|罗祥德
王李廷煜|TMI 2024|[Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation](https://ieeexplore.ieee.org/document/10167829)|| 39 | |2023.12.18|李赫
刘鑫雅|MIA 2023|[Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation](https://www.sciencedirect.com/science/article/abs/pii/S1361841522002845)|| 40 | |2023.12.11|叶平
曲义杰|CVPR 2023|[Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_Bidirectional_Copy-Paste_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/DeepMed-Lab-ECNU/BCP)| 41 | |2023.12.04|魏洁
邹宝胜|CVPR 2023|[SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Xiang_SQUID_Deep_Feature_In-Painting_for_Unsupervised_Anomaly_Detection_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/tiangexiang/SQUID)| 42 | |2023.11.27|卢江山
韩梦|MICCAI 2023|1. [A Style Transfer-Based Augmentation Framework for Improving Segmentation and Classification Performance Across Different Sources in Ultrasound Images](https://link.springer.com/chapter/10.1007/978-3-031-43987-2_5)
2. [Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification](https://link.springer.com/chapter/10.1007/978-3-031-43898-1_13)|
[Pytorch](https://github.com/VIROBO-15/XM-GAN)| 43 | |2023.11.20|伍江浩
周渝博|MICCAI 2023|1. [Multi-Target Domain Adaptation with Prompt Learning for Medical Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-43907-0_68)
2. [Self-Supervised Domain Adaptive Segmentation of Breast Cancer via Test-Time Fine-Tuning](https://link.springer.com/chapter/10.1007/978-3-031-43907-0_52)|[Pytorch](https://github.com/MurasakiLin/prompt-DA)| 44 | |2023.11.13|付佳
钟岚烽|MICCAI 2023|1. [PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images](https://link.springer.com/chapter/10.1007/978-3-031-43895-0_6)
2. [OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification](https://link.springer.com/chapter/10.1007/978-3-031-43895-0_1)|[Pytorch](https://github.com/CrystalWei626/PLD_AL)
[Pytorch](https://github.com/miccaiif/OpenAL)| 45 | |2023.11.06|罗祥德
王李廷煜|MICCAI 2023|1. [UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner](https://link.springer.com/chapter/10.1007/978-3-031-43898-1_49)
2. [Ariadne's Thread: Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images](https://link.springer.com/chapter/10.1007/978-3-031-43901-8_69)|[Pytorch](https://github.com/yeerwen/UniSeg)
[Pytorch](https://github.com/Junelin2333/LanGuideMedSeg-MICCAI2023)| 46 | |2023.10.30|李赫
刘鑫雅|MICCAI 2023|1. [Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-43901-8_2)
2. [Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher](https://link.springer.com/chapter/10.1007/978-3-031-43907-0_65)|[Pytorch](https://github.com/ShishuaiHu/CCSDG/tree/master)
[Pytorch](https://github.com/lloongx/SFDA-CBMT/tree/main)| 47 | |2023.10.23|曲义杰
叶平|MICCAI 2023|1. [Weakly Supervised Medical Image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization](https://link.springer.com/chapter/10.1007/978-3-031-43895-0_13)
2. [Decoupled Consistency for Semi-supervised Medical Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-43907-0_53)|[]()
[Pytorch](https://github.com/wxfaaaaa/DCNet)| 48 | |2023.10.16|邹宝胜
魏洁|MICCAI 2023|1. [self-supervised learning for endoscopic video analysis](https://link.springer.com/chapter/10.1007/978-3-031-43904-9_55)
2. [Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-supervised Contrastive Learning](https://link.springer.com/chapter/10.1007/978-3-031-43987-2_51)|[]()
[Pytorch](https://github.com/ladderlab-xjtu/forensic_pathology)| 49 | |2023.10.09|韩梦
卢江山|Nature Communications 2021|[Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images](https://www.nature.com/articles/s41467-021-26643-8#Sec23)|[Tensorflow](https://zenodo.org/record/5524324#.YU09Ny-KFLY)| 50 | |2023.09.25|伍江浩
周渝博|CVPR 2023|[Siamese Image Modeling for Self-Supervised Vision Representation Learning](https://openaccess.thecvf.com/content/CVPR2023/papers/Tao_Siamese_Image_Modeling_for_Self-Supervised_Vision_Representation_Learning_CVPR_2023_paper.pdf)|| 51 | |2023.08.07|付佳
钟岚烽|Nature Biomedical Engineering 2023|[Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging](https://www.nature.com/articles/s41551-023-01049-7)|[Tensorflow](https://github.com/google-research/medical-ai-research-foundations/tree/main)| 52 | |2023.07.24|罗祥德
王李廷煜|Nature Machine Intelligence 2023|[Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation](https://www.nature.com/articles/s42256-023-00682-w)|[Pytorch](https://github.com/himashi92/Co-BioNet)| 53 | |2023.07.17|伍江浩
周瑜博|IPMI 2023|[Human-Machine Interactive Tissue Prototype Learning for Label-Efficient Histopathology Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-34048-2_52)|[Pytorch](https://github.com/WinterPan2017/proto2seg)| 54 | |2023.07.10|魏洁
邹宝胜|CVPR 2023|[Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Uncertainty-Aware_Optimal_Transport_for_Semantically_Coherent_Out-of-Distribution_Detection_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/LuFan31/ET-OOD)| 55 | |2023.07.03|韩梦
卢江山|CVPR 2023|[Pseudo-label Guided Contrastive Learning for Semi-supervised Medical Image Segmentation](https://openaccess.thecvf.com/content/CVPR2023/papers/Basak_Pseudo-Label_Guided_Contrastive_Learning_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/hritam-98/PatchCL-MedSeg)| 56 | |2023.06.26|叶平
曲义杰|CVPR 2022|[BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_BoostMIS_Boosting_Medical_Image_Semi-Supervised_Learning_With_Adaptive_Pseudo_Labeling_CVPR_2022_paper.pdf)|[Pytorch](https://github.com/wannature/BoostMIS)| 57 | |2023.06.19|付佳
钟岚烽|CVPR 2023|[Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Revisiting_Weak-to-Strong_Consistency_in_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf)|[Pytorch](https://github.com/LiheYoung/UniMatch)| 58 | |2023.06.19|罗祥德
钟岚烽|CVPR 2023|[Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Weakly_Supervised_Segmentation_With_Point_Annotations_for_Histopathology_Images_via_CVPR_2023_paper.pdf)|[Not available](https://github.com/hrzhang1123/CVM_WS_Segmentation)| 59 | |2023.05.22|付佳
钟岚烽|ICLR 2023|[Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation](https://arxiv.org/pdf/2209.14566.pdf)|[Pytorch](https://github.com/boahK/DARL)| 60 | |2023.05.15|韩梦
卢江山|CVPR 2022|[CRIS: CLIP-Driven Referring Image Segmentation](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.pdf)|| 61 | |2023.05.08|刘鑫雅
伍江浩|CVPR 2023|[Delving into Shape-aware Zero-shot Semantic Segmentation](https://arxiv.org/pdf/2304.08491.pdf)|[Pytorch](https://github.com/Liuxinyv/SAZS)| 62 | |2023.04.24|魏洁
翟书唯|CVPR 2022|[High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/pdf/2112.10752.pdf)|[Pytorch](https://github.com/CompVis/latent-diffusion)| 63 | |2023.04.17|顾然
Dejene|arXiv|[Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/pdf/2303.05499.pdf)|[Pytorch](https://github.com/IDEA-Research/GroundingDINO)| 64 | |2023.04.10|罗祥德
赵乾飞|arXiv|[Segment Anything](https://arxiv.org/pdf/2304.02643.pdf)|[Pytorch](https://github.com/facebookresearch/segment-anything)| 65 | |2023.04.03|邹宝胜
曲义杰|CVPR 2022|[GroupViT: Semantic Segmentation Emerges from Text Supervision](https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_GroupViT_Semantic_Segmentation_Emerges_From_Text_Supervision_CVPR_2022_paper.pdf)|[Pytorch](https://github.com/NVlabs/GroupViT)| 66 | |2023.03.27|韩梦
卢江山|arXiv|[CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation](https://arxiv.org/pdf/2212.09506.pdf)|[Pytorch](https://github.com/linyq2117/CLIP-ES)| 67 | |2023.03.20|付佳
钟岚烽|ECCV 2022|[A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136890725.pdf)|[Pytorch](https://github.com/MendelXu/zsseg.baseline)| 68 | |2023.03.16|赵乾飞
邹宝胜|arXiv|[CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://arxiv.org/abs/2301.00785v1)|[Pytorch](https://github.com/ljwztc/CLIP-Driven-Universal-Model)| 69 | |2023.03.02|伍江浩
刘鑫雅|AAAI 2023|[Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation](https://arxiv.org/abs/2212.04145)|| 70 | |2023.02.23|曲义杰
董桂铭|CVPR 2022|[Decoupled Knowledge Distillation](https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Decoupled_Knowledge_Distillation_CVPR_2022_paper.html)|[PyTorch](https://github.com/megvii-research/mdistiller)| 71 | |2023.02.16|魏洁
翟书唯|TMI 2021|[Anomaly Detection for Medical Images Using Self-Supervised and Translation-Consistent Features](https://ieeexplore.ieee.org/document/9469869)|| 72 | |2022.12.29|罗祥德
付佳|MIA 2022|[Segmentation with mixed supervision: Confidence maximization helps knowledge distillation](https://www.sciencedirect.com/science/article/abs/pii/S1361841522002985)|[Pytorch](https://github.com/by-liu/ConfKD)| 73 | |2022.12.22|顾然
Dejene|MIA 2022|[Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation](https://www.sciencedirect.com/science/article/abs/pii/S1361841522002511)|[Pytorch](https://github.com/YonghengSun1997/ODADA)| 74 | |2022.12.08|刘鑫雅
董桂铭|TMI 2022|[DLTTA: Dynamic Learning Rate for Test-Time Adaptation on Cross-Domain Medical Images](https://ieeexplore.ieee.org/document/9830762)|[Pytorch](https://github.com/med-air/DLTTA)| 75 | |2022.12.01|韩梦
卢江山|TMI 2022|[SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://ieeexplore.ieee.org/document/9740182)|| 76 | |2022.11.24|付佳
钟岚烽|MedIA 2022|[Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning](https://www.sciencedirect.com/science/article/pii/S1361841522000925)|[Pytorch](https://github.com/DeepMedLab/Tri-U-MT)| 77 | |2022.11.17|魏洁
翟书唯|MedIA 2022|[Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation](https://www.sciencedirect.com/science/article/pii/S1361841522002298)|| 78 | |2022.11.10|伍江浩
邹宝胜|TMI (Early Access)|[LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation](https://ieeexplore.ieee.org/abstract/document/9919170)|| 79 | |2022.11.03|曲义杰
赵乾飞|MedIA 2022|[Mutual consistency learning for semi-supervised medical image segmentation](https://www.sciencedirect.com/science/article/pii/S1361841522001773?fr=RR-2&ref=pdf_download&rr=761bbb8b0a270461)|[Pytorch](https://github.com/ycwu1997/MC-Net)| 80 | |2022.10.27|卢江山|MICCAI 2022|[Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation](https://arxiv.org/pdf/2206.13737.pdf)|| 81 | |2022.10.27|刘鑫雅|MICCAI 2022|[Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift](https://arxiv.org/pdf/2207.00769.pdf)|[Pytorch](https://github.com/med-air/TTADC)| 82 | |2022.10.27|付佳|MICCAI 2022|[Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation](https://arxiv.org/pdf/2203.08878.pdf)|[Pytorch](https://github.com/pianoza/LayerEnsembles)| 83 | |2022.10.13|魏洁|MICCAI 2022|[Out-of-Distribution Detection for Long-Tailed and Fine-Grained Skin Lesion Images](https://arxiv.org/abs/2206.15186)|[Pytorch](https://github.com/DevD1092/ood-skin-lesion)| 84 | |2022.10.13|曲义杰|MICCAI 2022|[DeSD: Self-Supervised Learning with DeepSelf-Distillation for 3D Medical ImageSegmentation](https://link.springer.com/chapter/10.1007/978-3-031-16440-8_52)|[Code](https://github.com/yeerwen/DeSD)| 85 | |2022.10.13|Dejene|MICCAI 2022|[Leveraging Labeling Representations in Uncertainty-Based Semi-supervised Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-16452-1_26)|| 86 | |2022.10.08|钟岚峰|MICCAI 2022|[Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_1)|| 87 | |2022.10.08|伍江浩|MICCAI 2022|[Test-Time Adaptation with Shape Moments for Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-16440-8_70)|[Pytorch](https://github.com/mathilde-b/TTA)| 88 | |2022.10.08|顾然|MICCAI 2022|[Domain Specific Convolution and High Frequency Reconstruction Based Unsupervised Domain Adaptation for Medical Image Segmentation](https://link.springer.com/chapter/10.1007/978-3-031-16449-1_62)|[Pytorch](https://github.com/ShishuaiHu/DoCR)| 89 | |2022.09.29|邹宝胜|MICCAI 2022|[Momentum Contrastive Voxel-Wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation](https://arxiv.org/pdf/2105.07059.pdf)|| 90 | |2022.09.29|罗祥德|MICCAI 2022|[Semi-supervised Medical Image Segmentation Using Cross-Model Pseudo-Supervision with Shape Awareness and Local Context Constraints](https://link.springer.com/content/pdf/10.1007/978-3-031-16452-1_14.pdf)|[Pytorch](https://github.com/igip-liu/SLC-Net/tree/main/code)| 91 | |2022.09.29|韩梦|MICCAI 2022|[Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations](https://link.springer.com/content/pdf/10.1007/978-3-031-16452-1_23.pdf)|[Pytorch](https://github.com/Qybc/Scribble2D5)| 92 | |2022.08.31|罗祥德|The Lancet Digital Health |[Spatially-aware Graph Neural Networks Enable Cross-level Molecular Profile Prediction in Colon Cancer Histopathology: A Retrospective Multicentre Cohort Study ](https://www.sciencedirect.com/science/article/pii/S2589750022001686)|| 93 | |2022.08.09|董桂铭
邹宝胜|Nature communications|[A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images](https://www.nature.com/articles/s41467-020-19527-w)|| 94 | |2022.08.02|顾然
Dejene|Nature communications|[A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images](https://www.nature.com/articles/s41467-022-29637-2)|| 95 | |2022.07.26|曲义杰
赵乾飞|Nature communications|[Annotation-efficient deep learning for automatic medical image segmentation](https://doi.org/10.1038/s41467-021-26216-9)|| 96 | |2022.07.19|卢江山
韩梦|Nature communications|[COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images](https://www.nature.com/articles/s41598-022-06854-9#Sec2)|| 97 | |2022.07.12|魏洁
翟书唯|Nature communications|[Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation](https://www.nature.com/articles/s41467-022-31514-x)|| 98 | |2022.07.05|刘鑫雅
伍江浩|Nature communications|[Glass-cutting medical images via a mechanical image segmentation method based on crack propagation](https://www.nature.com/articles/s41467-020-19392-7#code-availability)|| 99 | |2022.06.28|钟岚烽
付佳|Nature communications|[Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations](https://www.nature.com/articles/s42256-022-00472-w.pdf)|| 100 | |2022.06.15|罗祥德|arxiv|[CT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training](https://arxiv.org/pdf/2206.02288.pdf)|| 101 | |2022.06.08|董桂铭
邹宝胜|arxiv|[NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition](https://arxiv.org/abs/2111.12994)|| 102 | |2022.06.01|卢江山
韩梦|arxiv|[Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation](https://arxiv.org/pdf/2203.10739.pdf)|| 103 | |2022.05.25|曲义杰
赵乾飞|arxiv|[Self-Distillation from the Last Mini-Batch for Consistency Regularization](https://arxiv.org/pdf/2203.16172.pdf)|| 104 | |2022.05.18|顾然
Dejene|AAAI2022|[Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary](https://aaai-2022.virtualchair.net/poster_aaai852)|| 105 | |2022.05.11|向东海
魏洁|arxiv|[ViM: Out-Of-Distribution with Virtual-logit Matching](https://arxiv.org/abs/2203.10807)|| 106 | |2022.04.28|付浩
翟书唯|arxiv|[CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://arxiv.org/pdf/2203.01475.pdf)|| 107 | |2022.04.21|付佳
钟岚烽|arxiv|[Adaptive Early-Learning Correction for Segmentation from Noisy Annotations](https://arxiv.org/pdf/2110.03740.pdf)|| 108 | |2022.04.14|郭栋
王璐|CVPR2019|[Semantic Image Synthesis with Spatially-Adaptive Normalization](https://arxiv.org/pdf/1903.07291.pdf)|| 109 | |2022.04.07|罗祥德
邹宝胜|arxiv|[Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization](https://arxiv.org/pdf/2112.11177.pdf)|| 110 | |2021.08.03|顾然
罗祥德|CVPR2021|[Uncertainty Reduction for Model Adaptation in Semantic Segmentation](https://openaccess.thecvf.com/content/CVPR2021/html/S_Uncertainty_Reduction_for_Model_Adaptation_in_Semantic_Segmentation_CVPR_2021_paper.html)|| 111 | |2021.07.28|董桂铭
邹宝胜|CVPR2021|[ Involution: Inverting the Inherence of Convolution for Visual Recognition](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Involution_Inverting_the_Inherence_of_Convolution_for_Visual_Recognition_CVPR_2021_paper.pdf)|| 112 | |2021.07.21|Dejene
伍江浩|CVPR2021|[Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://arxiv.org/abs/2012.15840 )|| 113 | |2021.07.14|赵乾飞
曲义杰|CVPR2021|[Coordinate Attention for Efficient Mobile Network Design](https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.pdf)|| 114 | |2021.07.07|向东海
杨硕崛|CVPR2021|[CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild](https://openaccess.thecvf.com/content/CVPR2021/papers/Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper.pdf )|| 115 | |2021.03.31|顾然
Dejene|arxiv|[Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://arxiv.org/abs/2104.05833)|| 116 | |2021.03.24|曲义杰
赵乾飞|CVPR2021|[Revisiting Knowledge Distillation: An Inheritance and Exploration Framework](https://arxiv.org/pdf/2107.00181.pdf)|| 117 | |2021.01.06|向东海
许伟 |TPAMI2020|[3D Hand Pose Estimation Using Synthetic Data and Weakly Labeled RGB Images](https://ieeexplore.ieee.org/document/9091090)|| 118 | |2020.12.30|董桂铭
王璐 |TMI|[Multi-Domain Image Completion for Random Missing Input Data](https://ieeexplore.ieee.org/document/9302720)|| 119 | |2020.12.23|杨硕崛
罗祥德|ECCV2020|[End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872)|| 120 | |2020.12.16|雷文辉
赵乾飞|MedIA|[‘Squeeze & excite’ guided few-shot segmentation of volumetric images](https://www.sciencedirect.com/science/article/pii/S1361841519301276)|| 121 | |2020.12.09|翟书唯
王欢 |TMI|[Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders](https://ieeexplore.ieee.org/document/9126830)|| 122 | |2020.11.25|向东海|ECCV2020 |[JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image](https://arxiv.org/abs/2007.04646)|| 123 | |2020.11.25|向东海|Arxiv |[Pixel-wise Regression: 3D Hand Pose Estimation via Spatial-form Representation and Differentiable Decoder](https://arxiv.org/abs/1905.02085)|| 124 | |2020.11.25|郭栋 |MICCAI2020 |[Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline](https://link.springer.com/chapter/10.1007/978-3-030-59728-3_57)|| 125 | |2020.11.25|郭栋 |MICCAI2020 |[Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network](https://link.springer.com/chapter/10.1007/978-3-030-59713-9_67)|| 126 | |2020.11.18|王璐 |MICCAI2020 |[Lesion Mask-Based Simultaneous Synthesis of Anatomic and Molecular MR Images Using a GAN](https://link.springer.com/chapter/10.1007/978-3-030-59713-9_11)|| 127 | |2020.11.18|王璐 |MICCAI2020 |[Graded Image Generation Using Stratified CycleGAN](https://link.springer.com/chapter/10.1007%2F978-3-030-59713-9_73)|| 128 | |2020.11.18|王璐 |MICCAI2020 |[AGAN: An Anatomy Corrector Conditional Generative Adversarial Network](https://link.springer.com/chapter/10.1007/978-3-030-59713-9_68)|| 129 | |2020.11.18|王欢 |MICCAI2020 |[Robust Medical Image Segmentation from Non-expert Annotations with Tri-network](https://link.springer.com/chapter/10.1007/978-3-030-59719-1_25)|| 130 | |2020.11.18|王欢 |MICCAI2020 |[Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation](https://link.springer.com/chapter/10.1007/978-3-030-59725-2_56)|| 131 | |2020.11.11|许伟 |CVPR2020 |[GaitPart: Temporal Part-Based Model for Gait Recognition](https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.html)|[PyTorch](https://github.com/ChaoFan96/GaitPart)| 132 | |2020.11.11|付浩 |MICCAI2020 |[Unsupervised Learning Model for Registration of Multi-phase Ultra-Widefield Fluorescein Angiography](https://www.springerprofessional.de/en/unsupervised-learning-model-for-registration-of-multi-phase-ultr/18442980)|| 133 | |2020.11.11|付浩 |MICCAI2020 |[Semantic Hierarchy Guided Registration Networks for Intra-subject Pulmonary CT Image Alignment](https://link.springer.com/chapter/10.1007/978-3-030-59716-0_18)|| 134 | |2020.11.11|付浩 |MICCAI2020 |[Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://arxiv.org/abs/2006.16148)|| 135 | |2020.11.04|翟书唯|TPAMI |[Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://ieeexplore.ieee.org/document/8417973)|| 136 | |2020.10.28|董桂铭
罗祥德|Arxiv|[Thickened 2D Networks for Efficient 3D Medical Image Segmentation](https://arxiv.org/abs/1904.01150)|| 137 | |2020.10.21|赵乾飞
雷文辉|TMI |[Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT](https://ieeexplore.ieee.org/document/8494708)|| 138 | |2020.10.14|杨硕崛
郭栋|MIA |[Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation](https://www.sciencedirect.com/science/article/pii/S1361841520301304)|| 139 | |2020.09.30|向东海|ICCV2019 |[A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image](https://arxiv.org/abs/1908.09999)|[Pytorch](https://github.com/zhangboshen/A2J)| 140 | |2020.09.02|梅昊陈|ECCV2020 |[Semi-Supervised Segmentation based on Error-Correcting Supervision](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740137.pdf)|| 141 | |2020.09.02|付浩 |CVPR2020 |[CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentationvia Global and Local Refinement](http://hkchengad.student.ust.hk/CascadePSP/CascadePSP.pdf)|[Pytorch](https://github.com/hkchengrex/CascadePSP)| 142 | |2020.08.19|王欢 |CVPR2020 |[Squeeze-and-Attention Networks for Semantic Segmentation](https://arxiv.org/abs/1909.03402)|| 143 | |2020.08.19|王欢 |CVPR2020 |[ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://arxiv.org/abs/1910.03151v4)|| 144 | |2020.08.19|王欢 |CVPR2020 |[Dynamic Convolution: Attention over Convolution Kernels](https://arxiv.org/abs/1912.03458)|| 145 | |2020.08.19|顾然 |CVPR2020 |[Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision](https://arxiv.org/pdf/2004.07703.pdf)|[Pytorch](https://github.com/feipan664/IntraDA)| 146 | |2020.08.12|雷文辉|CVPR2020 |[Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search](https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Organ_at_Risk_Segmentation_for_Head_and_Neck_Cancer_Using_CVPR_2020_paper.html)|| 147 | |2020.08.12|郭栋 |CVPR2020 |[MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Karnewar_MSG-GAN_Multi-Scale_Gradients_for_Generative_Adversarial_Networks_CVPR_2020_paper.pdf)|| 148 | |2020.08.05|王璐 |ECCV2020 |[Unsupervised Sketch-to-Photo Synthesis](https://arxiv.org/abs/1909.08313)|| 149 | |2020.08.05|刘保森|ECCV2020 |[BorderDet: Border Feature for Dense Object Detection](https://arxiv.org/abs/2007.11056)|[Pytorch](https://github.com/Megvii-BaseDetection/BorderDet)| 150 | |2020.07.29|许伟 |CVPR2020 |[PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation](https://openaccess.thecvf.com/content_CVPR_2020/html/Benzine_PandaNet_Anchor-Based_Single-Shot_Multi-Person_3D_Pose_Estimation_CVPR_2020_paper.html)|| 151 | |2020.07.29|向东海|CVPR2020 |[Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation](https://arxiv.org/abs/2004.00329)|[Pytorch](https://github.com/fabbrimatteo/LoCO)| 152 | |2020.07.22|王欢 |CVPR2020 |[Learning Dynamic Routing for Semantic Segmentation](https://arxiv.org/abs/2003.10401)|| 153 | |2020.07.22|罗祥德|CVPR2020 |[Structure Boundary Preserving Segmentation for Medical Imagewith Ambiguous Boundary](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_Structure_Boundary_Preserving_Segmentation_for_Medical_Image_With_Ambiguous_Boundary_CVPR_2020_paper.pdf)|| 154 | |2020.07.15|梅昊陈|MIDL |[1. Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision](https://openreview.net/pdf?id=m7HZ-yil_-) [2. Mutual information deep regularization for semi-supervised segmentation](https://openreview.net/pdf?id=iunvffXgPm)|| 155 | |2020.07.15|付浩 |MIDL |[1. Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation](https://openreview.net/forum?id=F1MIJCqX2J) [2. A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy](https://arxiv.org/abs/2004.08122)|| 156 | |2020.07.08|雷文辉|Nature machine intelligence|[Predicting tumour mutational burden from histopathological images using multiscale deep learning](https://sci-hub.tw/10.1038/s42256-020-0190-5)|| 157 | |2020.07.08|郭栋 |Nature machine intelligence|[Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN](https://arxiv.org/abs/1905.04105)|| 158 | |2020.07.01|王璐|Nature machine intelligence|[Augmenting Vascular Disease Diagnosis by Vasculature-aware Unsupervised Learning](https://www.biorxiv.org/content/10.1101/2020.02.07.938282v1.full.pdf)|| 159 | |2020.07.01|顾然|Nature machine intelligence|[A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets](https://www.nature.com/articles/s42256-020-0173-6)| [Python](https://codeocean.com/capsule/5978670/tree/v1)| 160 | |2020.06.24|刘保森|TPAMI |[End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning](https://arxiv.org/abs/1808.03405)|| 161 | |2020.06.17|许伟 |CVPR2020 |[HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://arxiv.org/abs/1908.10357)|[Pytorch](https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation)| 162 | |2020.06.17|向东海|CVPR2020 |[VIBE: Video Inference for Human Body Pose and Shape Estimation](https://arxiv.org/abs/1912.05656)| [Tensorflow](https://github.com/mkocabas/VIBE)| 163 | |2020.06.10|梅昊陈|TMI|[Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://arxiv.org/abs/2004.14133)|[Pytorch](https://github.com/DengPingFan/Inf-Net)| 164 | |2020.06.10|付浩|Neural Networks|[Learning deformable registration of medical images with anatomical constraints](https://doi.org/10.1016/j.neunet.2020.01.023)|[Tensorflow](https://github.com/lucasmansilla/ACRN_Chest_X-ray_IA)| 165 | |2020.06.03|王欢 |Submitted to TMI|[Synergistic Learning of Lung Lobe Segmentationand Hierarchical Multi-Instance Classification forAutomated Severity Assessment of COVID-19 inCT Images](https://arxiv.org/pdf/2005.03832.pdf)|| 166 | |2020.06.03|雷文辉|TMI|[HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation](https://ieeexplore.ieee.org/document/8515234)|[Pytorch](https://github.com/josedolz/HyperDenseNet_pytorch)| 167 | |2020.05.27|郭栋 |CVPR2020 |[Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation](https://arxiv.org/abs/2003.00273)|[PyTorch](https://github.com/alpc91/NICE-GAN-pytorch)| 168 | |2020.05.27|顾然 |TMI |[Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://arxiv.org/abs/2002.02255)|[TensorFlow](https://github.com/cchen-cc/SIFA)| 169 | |2020.01.08|梅昊陈|TPAMI |[Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://arxiv.org/abs/1908.05724)|[Pytorch](https://github.com/sud0301/semisup-semseg)| 170 | |2019.12.25|刘保森|TPAMI |[Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans](https://ieeexplore.ieee.org/document/8187667)| | 171 | |2019.12.18|许伟 |TPAMI |[3D Human Pose Machines with Self-supervised Learning](https://arxiv.org/abs/1901.03798)|[Tensorflow](https://github.com/chanyn/3Dpose_ssl)| 172 | |2019.12.18|向东海|{IEEE} Trans.Mutimedia |[Multi-Person Pose Estimation Using Bounding Box Constraint and LSTM](https://ieeexplore.ieee.org/document/8662702?denied=)| | 173 | |2019.12.11|王璐 |TMI |[Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment](https://www.researchgate.net/publication/331600377_Retinal_Image_Synthesis_and_Semi-Supervised_Learning_for_Glaucoma_Assessment)| | 174 | |2019.12.11|雷文辉|MedIA |[Self-supervised learning for medical image analysis using image context restoration](https://www.sciencedirect.com/science/article/abs/pii/S1361841518304699)| | 175 | |2019.12.04|王欢 |TMI/Neurocomputing |[Recalibrating Fully Convolutional Networks with Spatial and Channel ‘Squeeze & Excitation’ Blocks](https://ieeexplore.ieee.org/abstract/document/8447284)| | 176 | |2019.12.04|付浩 |TMI2019 |[Progressively trained convolutional neural networks for deformable image registration](https://ieeexplore.ieee.org/abstract/document/8902170)| | 177 | |2019.11.27|罗祥德|TMI2019 |[A 3D Spatially-Weighted Network for Segmentation of Brain Tissue from MRI](https://ieeexplore.ieee.org/abstract/document/8811612)| | 178 | |2019.11.27|顾然 |TMI2019 |[Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation](https://arxiv.org/abs/1902.08128)| | 179 | |2019.11.20|付浩 |ICCV2019 |[Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://arxiv.org/abs/1907.12353)|[Tensorflow](https://github.com/microsoft/Recursive-Cascaded-Networks)| 180 | |2019.11.13|许伟 |ICCV2019 |[HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation](https://arxiv.org/abs/1910.12032)| | 181 | |2019.11.13|向东海|ICCV2019 |[Cross View Fusion for 3D Human Pose Estimation](https://arxiv.org/abs/1909.01203)|[Pytorch](https://github.com/microsoft/multiview-human-pose-estimation-pytorch)| 182 | |2019.11.06|刘保森|ICCV2019 |[SinGAN: Learning a Generative Model from a Single Natural Image](https://arxiv.org/abs/1905.01164)|[Pytorch](https://github.com/tamarott/SinGAN)| 183 | |2019.10.30|王璐 |MICCAI2019 |[ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation](https://arxiv.org/abs/1907.10936)|[Pytorch](https://github.com/ZzzjzzZ/ETNet)| 184 | |2019.10.30|郭栋 |MICCAI2019 |[Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network](https://link.springer.com/chapter/10.1007/978-3-030-32226-7_18)| | 185 | |2019.10.23|梅昊陈|MICCAI |[1.Improved Inference via Deep Input Transfer](https://arxiv.org/abs/1904.02307) [2.Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation](https://arxiv.org/abs/1907.11835)| | 186 | |2019.10.23|雷文辉|MICCAI2019 |[Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice](https://arxiv.org/abs/1911.01685)| | 187 | |2019.10.16|刘保森|CVPR2019 |[RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion](http://openaccess.thecvf.com/content_CVPR_2019/papers/Sarmad_RL-GAN-Net_A_Reinforcement_Learning_Agent_Controlled_GAN_Network_for_Real-Time_CVPR_2019_paper.pdf)| | 188 | |2019.10.16|郭栋 |CVPR2019 | [Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning](https://arxiv.org/abs/1905.01102) | [Pytorch](https://github.com/gidariss/wDAE_GNN_FewShot) | 189 | 190 | ## MICS-Lecture-Share 191 | 192 | MICS online academic lectures: [MICS_China](https://space.bilibili.com/269362804?spm_id_from=333.337.0.0) 193 | 194 | |Date|Speaker|Title|Video| 195 | |---|:---:|---|---| 196 | |2020.01.07|[高跃](http://www.gaoyue.org/cn/index)| Hypergraph Learning and its Applications | [Bilibili](https://www.bilibili.com/video/BV1nK4y1b7bZ) | 197 | |2020.02.18|刘士远、史河水、[沈定刚](http://shen.web.unc.edu/)等| 新冠肺炎影像诊断及AI系统进展 | [Bilibili](https://www.bilibili.com/video/BV19V411R7oE) | 198 | |2020.03.13|黄昆| 计算病理与基因组学数据的整合分析（计算病理专题报告之二） | [Bilibili](https://www.bilibili.com/video/BV1Za4y1v76N) | 199 | |2020.03.31|牛海军| 超声在生物组织力学特性测量方面的应用 | [Bilibili](https://www.bilibili.com/video/BV1qz411z7LD) | 200 | |2020.04.14|刘日升| 优化观点下的深度学习及其在医学影像领域中的应用 | [Bilibili](https://www.bilibili.com/video/BV11A41147w5) | 201 | |2020.04.28|[窦琪](http://www.cse.cuhk.edu.hk/~qdou/)| 深度学习在医学图像分割中的模型泛化性能研究 | [Bilibili](https://www.bilibili.com/video/BV1Ye411p7Vo) | 202 | |2020.05.14|[沈定刚](http://shen.web.unc.edu/)、刘天明、郑国焱等| Imaging AI based Management of COVID-19 Webinar Series | [Bilibili](https://www.bilibili.com/video/BV1Ji4y147qW?p=1) | 203 | |2020.06.02|[夏勇](https://teacher.nwpu.edu.cn/yongxia.html)| 医学影像小数据深度学习 | [Bilibili](https://www.bilibili.com/video/BV1wz4y1R7cj) | 204 | |2020.06.16|唐晓颖| 高度形变微分同胚度量映射及其在神经影像和计算机视觉中的应用 | [Bilibili](https://www.bilibili.com/video/BV1PK4y1476e) | 205 | |2020.06.30|高飞| 光声成像：硬件系统启发的算法设计 | [Bilibili](https://www.bilibili.com/video/BV1Lt4y1977M) | 206 | |2020.07.18-19|MICS| 第七届医学图像计算青年研讨会 |[blibili](https://www.bilibili.com/video/BV14v411q7Ct) | 207 | |2020.07.30|[柏文佳](https://www.imperial.ac.uk/people/w.bai)| 机器学习在心脏图像分析中的应用 | [Blibili](https://www.bilibili.com/video/BV1tv411v7d6) | 208 | |2020.08.11|郭翌| 基于深度学习的超声影像超分辨率重建及分析方法 | [Blibili](https://www.bilibili.com/video/BV1Lh411d73f) | 209 | |2020.08.25|[秦宸](https://www.eng.ed.ac.uk/about/people/dr-chen-qin)| 机器学习在核磁共振图像重构及分析中的研究 | [Blibili](https://www.bilibili.com/video/BV1Rz4y1f7xD) | 210 | |2020.09.08|[何旭明](https://plus.sist.shanghaitech.edu.cn)| 关于图像语义分割和对齐问题中的弱监督策略 | [Blibili](https://www.bilibili.com/video/BV1tf4y1X7Hu) | 211 | |2020.09.22|高盛华| Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images | [Blibili](https://www.bilibili.com/video/BV1TK4y1Y7cb) | 212 | 213 | **[⬆ back to top](#组内资料分享)** 214 | --------------------------------------------------------------------------------