Medical image segmentation faces critical challenges in semi-supervised learning scenarios due to severe annotation scarcity requiring expert radiological knowledge, significant inter-annotator variability across different viewpoints and expertise levels, and inadequate multi-scale feature integration for precise boundary delineation in complex anatomical structures. Existing semi-supervised methods demonstrate substantial performance degradation compared to fully supervised approaches, particularly in small target segmentation and boundary refinement tasks. To address these fundamental challenges, we propose SASNet (Scale-aware Adaptive Supervised Network), a dual-branch architecture that leverages both low-level and high-level feature representations through novel scale-aware adaptive reweight mechanisms. Our approach introduces three key methodological innovations, including the Scale-aware Adaptive Reweight strategy that dynamically weights pixel-wise predictions using temporal confidence accumulation, the View Variance Enhancement mechanism employing 3D Fourier domain transformations to simulate annotation variability, and segmentation-regression consistency learning through signed distance map algorithms for enhanced boundary precision. These innovations collectively address the core limitations of existing semi-supervised approaches by integrating spatial, temporal, and geometric consistency principles within a unified optimization framework. Comprehensive evaluation across LA, Pancreas-CT, and BraTS datasets demonstrates that SASNet achieves superior performance with limited labeled data, surpassing state-of-the-art semi-supervised methods while approaching fully supervised performance levels. The source code for SASNet is available at https://github.com/HUANGLIZI/SASNet.
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
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）
Annotation-efficient deep learning for automatic medical image segmentation
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory（琶洲实验室）；Guangdong General Hospital, Guangdong Academy of Medical Sciences（广东省人民医院（广东省医学科学院））；Renmin Hospital of Wuhan University（武汉大学人民医院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）；Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院脑认知与脑疾病研究所）；ETS Montreal（蒙特利尔高等技术学院）
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
Hebei University of Technology（河北工业大学）；UESTC（电子科技大学）；Zayed University of Artificial Intelligence（扎耶德人工智能大学）；University of Macau（澳门大学）；Sun Yat-sen University（中山大学）
Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation
Beihang University（北京航空航天大学）；Hefei Innovation Research Institute, Beihang University（北京航空航天大学合肥创新研究院）；Beijing Advanced Innovation Centre for Biomedical Engineering（北京生物医学工程高精尖创新中心）；Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine（北京大数据精准医疗高精尖创新中心）
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Data Science, Fudan University（复旦大学大数据学院）；School of Engineering Medicine, Beihang University（北京航空航天大学医工交叉创新研究院）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与产业研究院）；School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物与医学工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Beijing Anding Hospital, Capital Medical University（首都医科大学附属北京安定医院）；Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beihang University（北京航空航天大学生物力学与力学生物学教育部重点实验室）；Hefei Innovation Research Institute, Beihang University（北京航空航天大学合肥创新研究院）
Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data
University of Oxford（牛津大学）；UiT The Arctic University of Norway（挪威北极大学）；Sun Yat-sen University（中山大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
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）
Transformer-based Annotation Bias-aware Medical Image Segmentation
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）；Research and Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）
FSDA-DG: Improving Cross-Domain Generalizability of Medical Image Segmentation with Few Source Domain Annotations
School of Biomedical Engineering（生物医学工程学院）；College of Information Science and Technology（信息科学与技术学院）；State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）；School of Information（信息学院）；Yunnan Key Laboratory of Intelligent Systems and Computing（云南智能系统与计算重点实验室）；Guangdong Provincial Key Laboratory of Medical Image Processing（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology（广东省医学影像与诊断技术工程实验室）；Pazhou Lab（琶洲实验室）
Semi-Supervised Medical Image Segmentation via Dual Networks
AIPM, School of Artificial Intelligence, Guilin University of Electronic Technology（桂林电子科技大学人工智能学院AIPM）；College of Computer Science and Engineering, Jeddah University（吉达大学计算机科学与工程学院）
