For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical regions and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group theory for medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing such challenging safety-critical tasks.
Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training
Southeast University（东南大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Case Western Reserve University（凯斯西储大学）；Centre de Recherche en Information Biomédicale Sino-Français（中法生物医学信息研究中心）
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 Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
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）
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）
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）
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks
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）；University Hospital Essen（埃森大学医院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））
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）
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）
Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
University College London（伦敦大学学院）；Queen Mary University of London（伦敦玛丽女王大学）；University of Oxford（牛津大学）；University College London Hospitals NHS Trust（伦敦大学学院医院NHS信托基金会）；Imperial College Healthcare（帝国理工学院医疗保健）
Rethinking Post-Hoc Calibration in Semantic Segmentation
ICube Laboratory, CNRS UMR 7357, University of Strasbourg（斯特拉斯堡大学ICube实验室，CNRS UMR 7357）；CLCC Institut Strauss（斯特劳斯研究所）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Faculty of Mathematics and Computer Science, University of Heidelberg（海德堡大学数学与计算机科学学院）；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学院）；Pattern Analysis and Learning Group, Dept. of Radiation Oncology, Heidelberg University Hospital（海德堡大学医院放射肿瘤科模式分析与学习组）
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）
Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
MEGVII Research（旷视研究院）；Megvii Inc（旷视科技）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Center for Research on Intelligent Perception and Computing (CRIPAC)（智能感知与计算研究中心）；National Laboratory of Pattern Recognition (NLPR)（模式识别国家重点实验室）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）
IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）
Stratify or Die: Rethinking Data Splits in Image Segmentation
Machine Learning and Data Analytics Lab, FAU Erlangen-Nürnberg（FAU埃尔兰根-纽伦堡机器学习与数据分析实验室）；Department of Computer Science, University of Bayreuth（拜鲁特大学计算机科学系）
What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning, Technical University of Munich（慕尼黑工业大学慕尼黑机器学习中心）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河右岸医院）；ELLIS Institute Finland（芬兰ELLIS研究所）；Aalto University（阿尔托大学）；Imperial College London（伦敦帝国理工学院）
Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models
Stanford University（斯坦福大学）；Shenzhen University（深圳大学）；The Chinese University of Hong Kong（香港中文大学）；City University of Hong Kong（香港城市大学）；Renmin University of China（中国人民大学）
The Risk Shadow of Principal Component Analysis: When 99.9999% Variance Preservation Causes Catastrophic Decision Errors
Department of EECS, School of Engineering, UQTR, Canada（加拿大魁北克大学三河城分校工程学院电气工程与计算机科学系）；Learning and Game Theory Laboratory (LnG Lab), TIMADIE（学习与博弈论实验室（LnG Lab），TIMADIE）
