Looking at the CVPR 2024 Open Access Repository content you provided, I can help you analyze the papers and find those related to medical image segmentation.

First Step: Overview of CVPR 2024 Papers

You’ve shared a comprehensive list of accepted papers at CVPR 2024. The repository contains hundreds of papers covering diverse computer vision topics. Each paper entry includes the title, authors, and links to PDF, supplementary materials, and bibtex citation.

Second Step: Medical Image Segmentation Related Papers

I’ve searched through the titles for papers related to medical image segmentation and medical imaging foundation models. Here are the relevant papers I found:

1. “MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling”

   • Authors: Xuzhe Zhang, Yuhao Wu, Elsa Angelini, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_MAPSeg_Unified_Unsupervised_Domain_Adaptation_for_Heterogeneous_Medical_Image_Segmentation_Based_CVPR_2024_paper.pdf

2. “Diversified and Personalized Multi-rater Medical Image Segmentation”

   • Authors: Yicheng Wu, Xiangde Luo, Zhe Xu, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Diversified_and_Personalized_Multi-rater_Medical_Image_Segmentation_CVPR_2024_paper.pdf

3. “PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-wise Hardness”

   • Authors: Siyao Jiang, Huisi Wu, Junyang Chen, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_PH-Net_Semi-Supervised_Breast_Lesion_Segmentation_via_Patch-wise_Hardness_CVPR_2024_paper.pdf

4. “Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation”

   • Authors: Xin Fan, Xiaolin Wang, Jiaxin Gao, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Fan_Bi-level_Learning_of_Task-Specific_Decoders_for_Joint_Registration_and_One-Shot_CVPR_2024_paper.pdf

5. “CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data”

   • Authors: Wei Fang, Yuxing Tang, Heng Guo, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Fang_CycleINR_Cycle_Implicit_Neural_Representation_for_Arbitrary-Scale_Volumetric_Super-Resolution_of_CVPR_2024_paper.pdf

6. “Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework”

   • Authors: Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Phan_Decomposing_Disease_Descriptions_for_Enhanced_Pathology_Detection_A_Multi-Aspect_Vision-Language_CVPR_2024_paper.pdf

7. “Transcriptomics-guided Slide Representation Learning in Computational Pathology”

   • Authors: Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Jaume_Transcriptomics-guided_Slide_Representation_Learning_in_Computational_Pathology_CVPR_2024_paper.pdf

8. “Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images”

   • Authors: Chaoqin Huang, Aofan Jiang, Jinghao Feng, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Adapting_Visual-Language_Models_for_Generalizable_Anomaly_Detection_in_Medical_Images_CVPR_2024_paper.pdf

9. “MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant”

   • Authors: Chenlu Zhan, Yu Lin, Gaoang Wang, et al.
   • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_MedM2G_Unifying_Medical_Multi-Modal_Generation_via_Cross-Guided_Diffusion_with_Visual_CVPR_2024_paper.pdf

10. “Accurate Spatial Gene Expression Prediction by Integrating Multi-Resolution Features”

    • Authors: Youngmin Chung, Ji Hun Ha, Kyeong Chan Im, Joo Sang Lee
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Chung_Accurate_Spatial_Gene_Expression_Prediction_by_Integrating_Multi-Resolution_Features_CVPR_2024_paper.pdf

11. “Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning”

    • Authors: Jiahan Li, Jiuyang Dong, Shenjin Huang, et al.
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Virtual_Immunohistochemistry_Staining_for_Histological_Images_Assisted_by_Weakly-supervised_Learning_CVPR_2024_paper.pdf

12. “Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology”

    • Authors: Wenhao Tang, Fengtao Zhou, Sheng Huang, et al.
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Tang_Feature_Re-Embedding_Towards_Foundation_Model-Level_Performance_in_Computational_Pathology_CVPR_2024_paper.pdf

13. “Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding”

    • Authors: Zhiheng Cheng, Qingyue Wei, Hongru Zhu, et al.
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Cheng_Unleashing_the_Potential_of_SAM_for_Medical_Adaptation_via_Hierarchical_Decoding_CVPR_2024_paper.pdf

14. “Systematic Comparison of Semi-supervised and Self-supervised Learning for Medical Image Classification”

    • Authors: Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Systematic_Comparison_of_Semi-supervised_and_Self-supervised_Learning_for_Medical_Image_CVPR_2024_paper.pdf

15. “Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge”

    • Authors: Bo Zou, Shaofeng Wang, Hao Liu, et al.
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Zou_Teeth-SEG_An_Efficient_Instance_Segmentation_Framework_for_Orthodontic_Treatment_based_CVPR_2024_paper.pdf

16. “Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts”

    • Authors: Jiayi Chen, Benteng Ma, Hengfei Cui, Yong Xia
    • PDF: https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Think_Twice_Before_Selection_Federated_Evidential_Active_Learning_for_Medical_CVPR_2024_paper.pdf

These papers cover various aspects of medical image segmentation, including domain adaptation, multi-rater segmentation, semi-supervised approaches, breast lesion segmentation, and leveraging foundation models like SAM for medical applications. They address challenges specific to medical imaging such as domain shifts, limited labeled data, and integration with multi-modal medical data.

Would you like me to provide a more detailed analysis of any specific papers from this list?