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| Fair Federated Medical Image Segmentation via Client Contribution Estimation| [![GitHub Page](https://img.shields.io/badge/GitHub-Page-159957.svg)](https://github.com/NVIDIA/NVFlare/tree/main/research/fed-ce) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.16520-b31b1b.svg)](http://arxiv.org/abs/2303.16520) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=zV4as47EWzg) |
| Histopathology Whole Slide Image Analysis With Heterogeneous Graph Representation Learning | [![GitHub](https://img.shields.io/github/stars/HKU-MedAI/WSI-HGNN?style=flat)](https://github.com/HKU-MedAI/WSI-HGNN) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2307.04189-b31b1b.svg)](https://arxiv.org/abs/2307.04189) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=F47ureXZ7fo) |
| Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses | [![GitHub](https://img.shields.io/github/stars/JunbongJang/contour-tracking?style=flat)](https://github.com/JunbongJang/contour-tracking) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Jang_Unsupervised_Contour_Tracking_of_Live_Cells_by_Mechanical_and_Cycle_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.08364-b31b1b.svg)](http://arxiv.org/abs/2303.08364) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=lA9lUx9mriM) |
| Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=Oh-kgpe-S_Y) |
| Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=Oh-kgpe-S_Y) |
| RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction <br/> [![CVPR - Highlight](https://img.shields.io/badge/CVPR-Highlight-FFFF00)]() | [![GitHub Page](https://img.shields.io/badge/GitHub-Page-159957.svg)](https://correr-zhou.github.io/RepMode/) <br /> [![GitHub](https://img.shields.io/github/stars/Correr-Zhou/RepMode?style=flat)](https://github.com/Correr-Zhou/RepMode) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_RepMode_Learning_to_Re-Parameterize_Diverse_Experts_for_Subcellular_Structure_Prediction_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2212.10066-b31b1b.svg)](http://arxiv.org/abs/2212.10066) | :heavy_minus_sign: |
| Sparse Multi-Modal Graph Transformer With Shared-Context Processing for Representation Learning of Giga-Pixel Images | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Nakhli_Sparse_Multi-Modal_Graph_Transformer_With_Shared-Context_Processing_for_Representation_Learning_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.00865-b31b1b.svg)](http://arxiv.org/abs/2303.00865) | :heavy_minus_sign: |
| Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision | | | |
| Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology whole Slide Image Classification | | | |
| TINC: Tree-Structured Implicit Neural Compression | | | |
| Topology-guided Multi-Class Cell Context Generation for Digital Pathology | | | |
| Directional Connectivity-based Segmentation of Medical Images | | | |
| A Soma Segmentation Benchmark in Full Adult Fly Brain | | | |
| Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking | | | |
| Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | | | |
| DualRel: Semi-Supervised Mitochondria Segmentation from a Prototype Perspective | | | |
| SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation | | | |
| OCELOT: Overlapped Cell on Tissue Dataset for Histopathology | | | |
| Orthogonal Annotation Benefits Barely-Supervised Medical Image Segmentation | | | |
| Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Yan_Towards_Trustable_Skin_Cancer_Diagnosis_via_Rewriting_Models_Decision_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.00885-b31b1b.svg)](https://arxiv.org/abs/2303.00885) | :heavy_minus_sign: |
| Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification | [![GitHub](https://img.shields.io/github/stars/invoker-LL/WSI-finetuning?style=flat)](https://github.com/invoker-LL/WSI-finetuning) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Li_Task-Specific_Fine-Tuning_via_Variational_Information_Bottleneck_for_Weakly-Supervised_Pathology_Whole_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.08446-b31b1b.svg)](http://arxiv.org/abs/2303.08446) | :heavy_minus_sign: |
| TINC: Tree-Structured Implicit Neural Compression | [![GitHub](https://img.shields.io/github/stars/RichealYoung/TINC?style=flat)](https://github.com/RichealYoung/TINC) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2211.06689-b31b1b.svg)](http://arxiv.org/abs/2211.06689) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=7UZ1YOsCtrU) |
| Topology-Guided Multi-Class Cell Context Generation for Digital Pathology | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Abousamra_Topology-Guided_Multi-Class_Cell_Context_Generation_for_Digital_Pathology_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.02255-b31b1b.svg)](http://arxiv.org/abs/2304.02255) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=3a5Mi6W5Vcw) |
| Directional Connectivity-Based Segmentation of Medical Images | [![GitHub](https://img.shields.io/github/stars/Zyun-Y/DconnNet?style=flat)](https://github.com/Zyun-Y/DconnNet) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Directional_Connectivity-Based_Segmentation_of_Medical_Images_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2304.00145-b31b1b.svg)](http://arxiv.org/abs/2304.00145) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=C3xCL8nbEU4) |
| A Soma Segmentation Benchmark in Full Adult Fly Brain|:heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Liu_A_Soma_Segmentation_Benchmark_in_Full_Adult_Fly_Brain_CVPR_2023_paper.pdf) | :heavy_minus_sign: |
| Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Luo_Constrained_Evolutionary_Diffusion_Filter_for_Monocular_Endoscope_Tracking_CVPR_2023_paper.pdf) | :heavy_minus_sign: |
| Benchmarking Self-Supervised Learning on Diverse Pathology Datasets | [![GitHub](https://img.shields.io/github/stars/lunit-io/benchmark-ssl-pathology?style=flat)](https://github.com/lunit-io/benchmark-ssl-pathology) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Kang_Benchmarking_Self-Supervised_Learning_on_Diverse_Pathology_Datasets_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2212.04690-b31b1b.svg)](http://arxiv.org/abs/2212.04690) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=yjIY0Xf0naU) |
| DualRel: Semi-Supervised Mitochondria Segmentation From a Prototype Perspective | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Mai_DualRel_Semi-Supervised_Mitochondria_Segmentation_From_a_Prototype_Perspective_CVPR_2023_paper.pdf) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=un00jmcBAbQ) |
| SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Shin_SDC-UDA_Volumetric_Unsupervised_Domain_Adaptation_Framework_for_Slice-Direction_Continuous_Cross-Modality_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2305.11012-b31b1b.svg)](https://arxiv.org/abs/2305.11012) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=R2c8sxEdjN4) |
| OCELOT: Overlapped Cell on Tissue Dataset for Histopathology | [![GitHub](https://img.shields.io/github/stars/lunit-io/ocelot23algo?style=flat)](https://github.com/lunit-io/ocelot23algo) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.13110-b31b1b.svg)](http://arxiv.org/abs/2303.13110) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=V9RjvV9JrM8) |
| Orthogonal Annotation Benefits Barely-Supervised Medical Image Segmentation | [![GitHub](https://img.shields.io/github/stars/HengCai-NJU/DeSCO?style=flat)](https://github.com/HengCai-NJU/DeSCO) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com//content/CVPR2023/papers/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.13090-b31b1b.svg)](http://arxiv.org/abs/2303.13090) | [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=UjFFGfUDpkc) |
| DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting | | | |
| Interactive and Explainable Region-guided Radiology Report Generation | | | |
| A Loopback Network for Explainable Microvascular Invasion Classification | | | |
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