└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # MICCAI 2022 Paper with Codes 2 | 3 | This repository is still under construction. 4 | Please help yourself to join 😊. 5 | 6 | ## Contents 7 | - [1. Backbone](##1.-Backbone) 8 | - [2. Multi Task Learning](##2.-Multi-Task-Learning) 9 | - [3. Self Supervised Learning](##3.-Self-Supervised-Learning) 10 | - [4. Weakly Supervised Learning](##4.-Weakly-Supervised-Learning) 11 | - [5. Semi Supervised Learning](##5.-Semi-Supervised-Learning) 12 | - [6. Imbalanced Data](##6.-Imbalanced-Data) 13 | - [7. Multi Modal](##7.-Multi-Modal) 14 | - [8. Data Augmentation](##8.-Data-Augmentation) 15 | - [9. Knowledge Distillation](##9.-Knowledge-Distillation) 16 | 17 | --- 18 | ## 1. Backbone 19 | 20 | **UNeXt: MLP-based Rapid Medical Image Segmentation Network** 21 | - Paper: https://arxiv.org/abs/2203.04967 22 | - Code: https://github.com/jeya-maria-jose/UNeXt-pytorch 23 | - Project Website: https://jeya-maria-jose.github.io/UNext-web/ 24 | - Data Modality: Camera-acquired dermatologic images, Ultrasound images. 25 | - Task: Segmentation. 26 | 27 | **Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification** 28 | - Paper: https://link.springer.com/chapter/10.1007/978-3-031-16434-7_18 29 | - Code: https://github.com/HeLongHuang/SHGNN 30 | - Data Modality: Histology. 31 | - Task: 32 | 33 | --- 34 | ## 2. Multi Task Learning 35 | 36 | **TGANet: Text-guided Attention for Improved Polyp Segmentation** 37 | - Paper: https://arxiv.org/abs/2205.04280 38 | - Code: https://github.com/nikhilroxtomar/TGANet 39 | - Data Modality: Colonoscopy. 40 | - Task: Polyp segmentation. 41 | 42 | --- 43 | ## 3. Self Supervised Learning 44 | 45 | **mulEEG: A Multi-View Representation Learning on EEG Signals** 46 | - Paper: https://arxiv.org/abs/2204.03272 47 | - Code: https://github.com/likith012/mulEEG 48 | - Data Modality: Electroencephalogram (EEG) signals. 49 | - Task: Classification. 50 | 51 | **Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Ray** 52 | - Paper: https://arxiv.org/pdf/2206.03935.pdf 53 | - Code: https://github.com/caiyu6666/DDAD 54 | - Data Modality: X-Rays. 55 | - Task: Anomaly detection. 56 | 57 | **Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions** 58 | - Paper: https://arxiv.org/abs/2205.09292 59 | - Code: https://github.com/xmed-lab/DistillingSelf 60 | - Data Modality: Surgical video. 61 | 62 | **Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image** 63 | - Paper: https://arxiv.org/abs/2206.01856 64 | - Code: https://github.com/tacalvin/Poisson2Sparse 65 | - Data Modality: Fluorescence microscopy, MRI. 66 | - Task: Denosing. 67 | 68 | --- 69 | ## 4. Weakly Supervised Learning 70 | 71 | **Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images** 72 | - Paper: https://arxiv.org/abs/2206.06665 73 | - Code: https://github.com/xmed-lab/OEEM 74 | - Data Modality: 75 | - Task: 76 | 77 | **Transformer Based Multiple Instance Learning for Weakly Supervised Histopathology Image Segmentation** 78 | - Paper: https://arxiv.org/pdf/2205.08878 79 | - Code: https://github.com/Nexuslkl/Swin_MIL 80 | - Data Modality: Histology. 81 | - Task: Segmentation. 82 | 83 | **Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification** 84 | - Paper: https://link.springer.com/chapter/10.1007/978-3-031-16434-7_36 85 | - Code: https://github.com/machiraju-lab/UA-CNN 86 | - Data Modality: Histology. 87 | - Task: Classification. 88 | 89 | **SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological Image Analysis** 90 | - Paper: https://link.springer.com/chapter/10.1007/978-3-031-16434-7_7 91 | - Code: https://github.com/TencentAILabHealthcare/SETMIL.git 92 | - Data Modality: Histology. 93 | - Task: Classification. 94 | 95 | --- 96 | ## 5. Semi Supervised Learning 97 | 98 | **Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation** 99 | - Paper: https://arxiv.org/pdf/2203.01324.pdf 100 | - Code: https://github.com/ycwu1997/SS-Net 101 | - Data Modality: 102 | - Task: 103 | 104 | --- 105 | ## 6. Imbalanced Data 106 | **Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation** 107 | - Paper: https://arxiv.org/abs/2205.03644 108 | - Code: https://github.com/xmed-lab/CLD-Semi 109 | - Data Modality: 110 | - Task: 111 | 112 | **NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification** 113 | - Paper: https://arxiv.org/abs/2103.04053 114 | - Code: https://github.com/FBLADL/NVUM 115 | - Data Modality: 116 | - Task: 117 | 118 | --- 119 | ## 7. Multi Modal 120 | **mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation** 121 | - Paper: https://arxiv.org/abs/2206.02425 122 | - Code: https://github.com/YaoZhang93/mmFormer 123 | - Data Modality: 124 | - Task: 125 | 126 | **Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency** 127 | - Paper: https://arxiv.org/abs/2206.11826 128 | - Code: https://github.com/WeijieMax/CPC-Trans 129 | - Data Modality: 130 | - Task: 131 | 132 | --- 133 | ## 8. Data Augmentation 134 | **SapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation** 135 | - Paper: https://arxiv.org/abs/2206.02118 136 | - Code: https://github.com/BWGZK/ShapePU 137 | - Data Modality: 138 | - Task: 139 | 140 | **RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization** 141 | - Paper: https://arxiv.org/abs/2206.12694 142 | - Code: https://github.com/yiqings/RandStainNA 143 | - Data Modality: MRI 144 | - Task: Classification, Segmentation. 145 | 146 | **SUPER-IVIM-DC: Intra-voxel Incoherent Motion Based Fetal Lung Maturity Assessment from Limited DWI Data Using Supervised Learning Coupled with Data-Consistency** 147 | - Paper: https://arxiv.org/pdf/2206.03820 148 | - Code: https://github.com/TechnionComputationalMRILab/SUPER-IVIM-DC 149 | - Data Modality: DWI MRI. 150 | - Task: 151 | 152 | --- 153 | ## 9. Knowledge-Distillation 154 | **Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction** 155 | - Paper: https://link.springer.com/chapter/10.1007/978-3-031-16434-7_6 156 | - Code: https://github.com/zoedsy/DK_Topology_PCR 157 | - Data Modality: MRI 158 | - Task: 159 | 160 | --- 161 | ## Inclusion Critertion 162 | In this initial stage where MICCAI papers are not official published, 163 | we use the key world `MICCAI 2022` on github to search the associated respoitory. 164 | Afterwards, we primary check if there is a pre-print version on arXiv. 165 | --------------------------------------------------------------------------------