We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology whole slide images (WSIs) for input to a neural network on-the-fly. We prefetch these patches as needed during network training, avoiding the need for WSI preparation such as chopping/tiling. We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively.
Norwegian University of Science and Technology（挪威科技大学）；St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；SINTEF Digital（挪威科技工业研究院数字研究所）
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（丹娜-法伯癌症研究所）；Harvard University（哈佛大学）
Brigham and Women’s Hospital（布里格姆妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院布罗德研究所）；Department of Pathology（病理系）；Northwell Health（诺斯韦尔健康）；Department of Biomedical Informatics（生物医学信息学系）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Cancer Program（癌症项目）；Cancer Data Science（癌症数据科学）
Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation
School of Computer Science and Enginecring, Central South University（计算机科学与工程学院，中南大学）；School of Computer Science（计算机科学学院）；Enginecring, Central South University（工程学院，中南大学）
University of Warwick（华威大学）；University of Birmingham（伯明翰大学）；The Alan Turing Institute（艾伦·图灵研究所）；University Hospitals Coventry & Warwickshire（考文垂与沃里克郡大学医院）
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）
A Graph-Based Framework for Interpretable Whole Slide Image Analysis
School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Department of Computing, Imperial College London（伦敦帝国学院计算机系）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Munich Data Science Institute, Technical University of Munich（慕尼黑数据科学研究所）；Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich（慕尼黑技术大学医学与健康学院病理学研究所）；Weill Cornell Medicine, Cornell University（韦尔医学院，康奈尔大学）
The University of Tokyo（东京大学）；RIKEN（理化学研究所）；The Japanese Society of Pathology（日本病理学会）；Kyoto University（京都大学）；Tokyo Medical and Dental University（东京医科齿科大学）；National Institute of Informatics（国立信息学研究所）
