Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detecting tumorous lesions. However, existing mainstream MIL methods focus on modeling correlation between instances while overlooking the inherent diversity among instances. However, few MIL methods have aimed at diversity modeling, which empirically show inferior performance but with a high computational cost. To bridge this gap, we propose a novel MIL aggregation method based on diverse global representation (DGR-MIL), by modeling diversity among instances through a set of global vectors that serve as a summary of all instances. First, we turn the instance correlation into the similarity between instance embeddings and the predefined global vectors through a cross-attention mechanism. This stems from the fact that similar instance embeddings typically would result in a higher correlation with a certain global vector. Second, we propose two mechanisms to enforce the diversity among the global vectors to be more descriptive of the entire bag: (i) positive instance alignment and (ii) a novel, efficient, and theoretically guaranteed diversification learning paradigm. Specifically, the positive instance alignment module encourages the global vectors to align with the center of positive instances (e.g., instances containing tumors in WSI). To further diversify the global representations, we propose a novel diversification learning paradigm leveraging the determinantal point process. The proposed model outperforms the state-of-the-art MIL aggregation models by a substantial margin on the CAMELYON-16 and the TCGA-lung cancer datasets. The code is available at \url{https://github.com/ChongQingNoSubway/DGR-MIL}.
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
Zhejiang University（浙江大学）；The Hong Kong University of Science and Technology（香港科技大学）；Sir Run Run Shaw Hospital（邵逸夫医院）；Zhejiang Lab（之江实验室）；Ritsumeikan University（立命馆大学）
Tencent AI Lab（腾讯人工智能实验室）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；School of Science, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）理学院）
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）
SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
School of Big Data & Software Engineering, Chongqing University（重庆大学大数据与软件学院）；Fowler College of Business, San Diego State University（圣地亚哥州立大学福勒商学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
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）
Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
Radboud University Medical Center, Netherlands（拉德堡德大学医学中心，荷兰）；Oncode Institute, Netherlands（Oncode研究所，荷兰）；Faculty of Health Sciences and Medicine, University of Lucerne, Switzerland（健康科学与医学学院，卢塞恩大学，瑞士）
Mallinckrodt Institute of Radiology, Washington University School of Medicine（华盛顿大学医学院马林克罗特放射学研究所）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine（华盛顿大学医学院信息学、数据科学与生物统计学研究所）
