The deep neural network is a research hotspot for histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach one gigapixel and contains abundant tissue feature information, which needs to be divided into a lot of patches in the training and inference stages. This will lead to a long convergence time and large memory consumption. Furthermore, well-annotated data sets are also in short supply in the field of digital pathology. Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference. We use neural network to construct the search model and decision model of reinforcement learning agent respectively. The search model predicts the next action through the image features of different magnifications in the current field of view, and the decision model is used to return the predicted probability of the current field of view image. In addition, an expert-guided model is constructed by multi-instance learning, which not only provides rewards for search model, but also guides decision model learning by the knowledge distillation method. Experimental results show that our proposed method can achieve fast inference and accurate prediction of whole slide images without any pixel-level annotations.
Whole Slide Concepts: A Supervised Foundation Model For Pathological Images
Institute for Digital Medicine MEVIS（数字医学MEVIS研究所）；Fraunhofer（弗劳恩霍夫）；Institute for Toxicology and Experimental Medicine（毒理学与实验医学研究所）；Institute for Diagnostic and Interventional Radiology（诊断与介入放射学研究所）；Hannover Medical School（汉诺威医学院）
PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains
Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National Biomedical Imaging Center, College of Future Technology, Peking University（北京大学未来技术学院国家生物医学成像中心）
PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis
Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学庆元研究院）；Shanghai Innovation Institute（上海创新研究院）；Department of Pathology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院病理科）；Intelligent Pathology Institute, Division of Life Sciences and Medicine（生命科学与医学学院智能病理研究所）；Department of Pathology, Fudan University Shanghai Cancer Center（复旦大学上海癌症中心病理科）；Department of Oncology, Shanghai Medical College, Fudan University（复旦大学上海医学院肿瘤科）；Institute of Pathology, Fudan University（复旦大学病理研究所）；Department of Pathology, The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院病理科）
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）
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Vanderbilt University（范德堡大学）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Vanderbilt University Medical Center（范德堡大学医学中心）；Veterans Affairs Tennessee Valley Healthcare System（田纳西河谷退伍军人事务医疗系统）
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
