Whole slide images, with their gigapixel-scale panoramas of tissue samples, are pivotal for precise disease diagnosis. However, their analysis is hindered by immense data size and scarce annotations. Existing MIL methods face challenges due to the fundamental imbalance where a single bag-level label must guide the learning of numerous patch-level features. This sparse supervision makes it difficult to reliably identify discriminative patches during training, leading to unstable optimization and suboptimal solutions. We propose a spatially regularized MIL framework that leverages inherent spatial relationships among patch features as label-independent regularization signals. Our approach learns a shared representation space by jointly optimizing feature-induced spatial reconstruction and label-guided classification objectives, enforcing consistency between intrinsic structural patterns and supervisory signals. Experimental results on multiple public datasets demonstrate significant improvements over state-of-the-art methods, offering a promising direction.
Fourier Transform Multiple Instance Learning for Whole Slide Image Classification
Institute of Artificial Intelligence (IAI)（人工智能研究所）；College of Medicine, University of Central Florida（中央佛罗里达大学医学院）；Department of Computer Science, University of Central Florida（中央佛罗里达大学计算机科学系）
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The Chinese University of Hong Kong（香港中文大学）；the Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；Imsight Medical Technology Co., Ltd.（艾迈医疗科技有限公司）
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
Université Paris Cité（巴黎西岱大学）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；French University Institute (IUF)（法国大学研究院）
针对肝细胞癌 WSI 全肿瘤分割难题，提出分层 Transformer 架构 HiTrans，在 4096×4096 图像块上学习全局依赖并提升分割性能。
Dual-stream attention-guided learning for weakly supervised whole slide image classification
College of Computer Science and Technology, College of Data Science, Taiyuan University of Technology（太原科技大学计算机科学与技术学院、数据科学学院）；College of Humanities, Law and Foreign Languages, Taiyuan University of Technology（太原科技大学人文学院、法律与外语学院）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络安全学院）；School of Mathematics, Taiyuan University of Technology（太原科技大学数学学院）
Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification
The Chinese University of Hong Kong（香港中文大学）；Centre of AI in Medicine, Singapore（新加坡人工智能医学中心）；The Hong Kong University of Science and Technology（香港科学大学）；Nanyang Technological University（南洋理工大学）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学Lee Kong Chian医学学院）；MedVisAI Lab, Singapore（新加坡MedVisAI实验室）
Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Images
Korea University College of Medicine（高丽大学医学院）；Korea University Anam Hospital（高丽大学安岩医院）；Seegene Medical Foundation（Seegene医学财团）；Seoul National University（首尔大学）；Ewha Womans University（梨花女子大学）；The Catholic University of Korea（韩国天主教大学）
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
University of Science and Technology of China（中国科学技术大学）；Chinese Academy of Sciences（中国科学院）；West China Hospital, Sichuan University（四川大学华西医院）；Beihang University（北京航空航天大学）
A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis
Department of Electrical and Computer Engineering, University of Houston（电子与计算机工程系，休斯顿大学）；Department of Information Science Technology, University of Houston（信息科学与技术系，休斯顿大学）；Department of Neurology, College of Medicine, University of Florida（神经病学系，医学学院，佛罗里达大学）
Digital Environment Research Institute（数字环境研究所）；Queen Mary University of London（伦敦玛丽女王大学）；William Harvey Research Institute（威廉·哈维研究所）；The Hong Kong Polytechnic University（香港理工大学）
ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis
USTC（中国科学技术大学）；NUDT（南京理工大学）；SCNU（华南师范大学）；NTU（南洋理工大学）；Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing（安徽省生物医学成像与智能处理重点实验室）
CLRecogEye : Curriculum Learning towards exploiting convolution features for Dynamic Iris Recognition
Indian Institute of Technology Mandi, India（印度理工学院马尼分校）；Directorate of Forensic Services, Shimla, India（刑事司法服务局）；Norwegian University of Science and Technology (NTNU), Norway（挪威科学与技术大学（NTNU））
Spatial Transcriptomics as Images for Large-Scale Pretraining
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
