This project aims to break down large pathology images into small tiles and then cluster those tiles into distinct groups without the knowledge of true labels, our analysis shows how difficult certain aspects of clustering tumorous and non-tumorous cells can be and also shows that comparing the results of different unsupervised approaches is not a trivial task. The project also provides a software package to be used by the digital pathology community, that uses some of the approaches developed to perform unsupervised unsupervised tile classification, which could then be easily manually labelled.
The project uses a mixture of techniques ranging from classical clustering algorithms such as K-Means and Gaussian Mixture Models to more complicated feature extraction techniques such as deep Autoencoders and Multi-loss learning. Throughout the project, we attempt to set a benchmark for evaluation using a few measures such as completeness scores and cluster plots.
Throughout our results we show that Convolutional Autoencoders manages to slightly outperform the rest of the approaches due to its powerful internal representation learning abilities. Moreover, we show that Gaussian Mixture models produce better results than K-Means on average due to its flexibility in capturing different clusters. We also show the huge difference in the difficulties of classifying different types of pathology textures.
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
Institute of Pathology, Technical University of Munich（慕尼黑工业大学病理研究所）；Computer Aided Medical Procedures (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序（CAMP））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Human Pathology, Juntendo University Graduate School of Medicine（顺天堂大学医学研究生院人体病理学部）；The Hong Kong University of Science and Technology（香港科技大学）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
Virchow: A Million-Slide Digital Pathology Foundation Model
Paige；Microsoft Research（微软研究院）；NSW Health Pathology（新南威尔士州卫生病理学）；St George Hospital（圣乔治医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Rochester（罗切斯特大学）
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis
Imperial College London, Department of Electrical and Electronic Engineering, London, UK（帝国理工学院伦敦分校，电子与电气工程系）；Department of Pathology, Institut de Pathologie Multisite, Groupement Hospitalier Sud, Lyon University Hospital, Pierre-Bénite, France（病理学系，多站点病理研究所，南部医院集团，里昂大学医院，皮埃尔-贝内蒂，法国）；University of Lyon, Université Claude Bernard Lyon 1, Lyon, France（里昂大学， Claude Bernard 里昂第一大学，里昂，法国）
WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
Multimodal Whole Slide Foundation Model for Pathology
Mass General Brigham, Harvard Medical School（哈佛医学院，麻省总医院布里格姆医疗系统）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Broad Institute of Harvard and MIT（哈佛与麻省理工学院博德研究所）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑中心——德国环境与健康研究中心）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电气工程与计算机科学系）；Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology（麻省理工学院哈佛-麻省卫生科学与技术分部）；Department of Pathology, Pusan National University（釜山大学病理学系）；Department of Pathology and Laboratory Medicine, Emory University School of Medicine（埃默里大学医学院病理学与检验医学系）；Harvard Data Science Initiative, Harvard University（哈佛大学哈佛数据科学计划）；Department of Systems Biology, Harvard Medical School（哈佛医学院系统生物学系）；Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo（东京大学医学部预防医学系）
Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；University of Oxford（牛津大学）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；University of Washington（华盛顿大学）
Huron Digital Pathology（休伦数字病理学公司）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）；McMaster University（麦克马斯特大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
High-performance Data Management for Whole Slide Image Analysis in Digital Pathology
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；NVIDIA Corporation（英伟达公司）；Georgia State University（佐治亚州立大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Weill Cornell Medicine（威尔康奈尔医学院）
本文提出基于 ADIOS2 的数字病理 WSI 数据管理流水线，通过并行化补丁级 I/O 缩短检索时间，在 CPU 场景获两倍加速，并在 GPU 场景达到与 NVIDIA Magnum IO GDS 相当的性能。
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎大脑研究所（ICM））；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共援助医院）；Hôpital Pitié Salpêtrière（皮蒂耶-萨尔佩特里埃医院）
