Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cause domain shifts as large as, or even larger than, those between centers. To address this, we propose the "one image as one domain" (OIOD) hypothesis, which treats each image as a unique domain, enabling flexible and robust domain generalization. Based on this hypothesis, we develop a unified disentanglement-based domain generalization (UniDDG) framework, which simultaneously handles both multi-source and single-source domain generalization without requiring explicit domain labels. This approach simplifies training with a fixed architecture, independent of the number of source domains, reducing complexity and enhancing scalability. We decouple each input image into content representation and style code, then exchange and combine these within the batch for segmentation, reconstruction, and further disentanglement. By maintaining distinct style codes for each image, our model ensures thorough decoupling of content representations and style codes, improving domain invariance of the content representations. Additionally, we enhance generalization with expansion mask attention (EMA) for boundary preservation and style augmentation (SA) to simulate diverse image styles, improving robustness to domain shifts. Extensive experiments show that our method achieves Dice scores of 84.43% and 88.91% for multi-source to single-center and single-center generalization in optic disc and optic cup segmentation, respectively, and 86.96% and 88.56% for prostate segmentation, outperforming current state-of-the-art domain generalization methods, offering superior performance and adaptability across clinical settings.
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
University of Oxford（牛津大学）；Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）；Big Data Institute, University of Oxford（牛津大学大数据研究所）
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
Junia（朱尼亚学院）；Centrale Lille（里尔中央理工学院）；University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）；Université Polytechnique Hauts-de-France（上法兰西理工大学）；Université de Lille（里尔大学）；Ho Chi Minh City Open University（胡志明市开放大学）
VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Huazhong University of Science and Technology（华中科技大学）；Jiangsu Province Hospital of Chinese Medicine（江苏省中医院）
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
Southeast University（东南大学）；Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing（江苏省医学信息处理联合国际研究实验室）；Centre de Recherche en Information Biomédicale Sino-Français (CRIBs)（中法生物医学信息研究中心（CRIBs））
厚切片扫描的性质导致3D医学图像的严重切片间不连续性，并且vanilla 2D/3D卷积神经网络（CNN）无法以平衡的方式表示稀疏的切片间信息和密集的切片内信息，导致严重的切片间特征欠拟合（对于vanilla 2D CNN）和过度拟合来自远程切片的噪声（对于vanilla 3D CNN）。在这项工作中，提出了一种新的网状网（MNet），通过学习来平衡轴间的空间表示。1)我们的MNet通过将多维卷积深度嵌入基本模块，潜在地融合了大量的表示过程，使得表示过程的选择灵活，从而自适应地平衡稀疏切片间信息和密集切片内信息的表示。2)我们的MNet潜在地融合了每个基本模块内部的多维特征，同时利用2D（2D视图中易于识别的区域的高分割精度）和3D（3D器官轮廓的高平滑度）表示的优势，从而获得更准确的目标区域建模。在四个公共数据集（CT\&MR）上进行了综合实验，实验结果一致表明所提出的MNet方法优于其他方法.代码和数据集可在以下网址获得：https://github.com/zfdong-code/MNet
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
MILA (Quebec Artificial Intelligence Institute)（魁北克人工智能研究所MILA）；McGill University（麦吉尔大学）；NeuroRx Research（NeuroRx研究公司）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）
Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（计算机科学与工程系，香港科学与技术大学）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology（化学与生物工程系，香港科学与技术大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Northeastern University（东北大学）；Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University（东北大学医学影像智能计算教育部重点实验室）；Amii, University of Alberta（阿尔伯塔大学阿尔伯塔机器智能研究所）
Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methods
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health亥姆霍兹健康信息与数据科学学院）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院医学人工智能研究所）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学院科隆埃森癌症研究中心）
MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network
Tianjin University（天津大学）；Tianjin Key Laboratory of Cognitive Computing and Application（天津认知计算与应用重点实验室）；Tianjin Key Laboratory of Advanced Networking（天津先进网络重点实验室）；Tianjin International Engineering Institute, Tianjin University（天津大学国际工程师学院）；Tianjin Central Hospital of Gynecology Obstetrics（天津市中心妇产科医院）；Silesian University of Technology（西里西亚工业大学）；University of Alberta（阿尔伯塔大学）；Istinye University（伊斯坦布尔耶尼大学）
From CNN to Transformer: A Review of Medical Image Segmentation Models
University of Electronic Science and Technology of China（电子科技大学）；Deyang People’s Hospital（德阳市人民医院）；Sichuan Provincial People’s Hospital（四川省人民医院）；Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital（中国科学院四川转化医学研究医院）
