Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides, WSIs require considerable data volume, which makes the storage and maintenance of WSI records costly and unsustainable. To this end, this work presents the first investigation of lossless compression of WSI images. Interestingly, we find that most existing compression methods fail to compress the WSI images effectively. Furthermore, our analysis reveals that the failure of existing compressors is mainly due to information irregularity in WSI images. To resolve this issue, we developed a simple yet effective lossless compressor called WISE, specifically designed for WSI images. WISE employs a hierarchical encoding strategy to extract effective bits, reducing the entropy of the image and then adopting a dictionary-based method to handle the irregular frequency patterns. Through extensive experiments, we show that WISE can effectively compress the gigapixel WSI images to 36 times on average and up to 136 times.
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）
Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images
Center for Applied Artificial Intelligence, University of Kentucky（应用人工智能中心，肯塔基大学）；Sanders-Brown Center on Aging, University of Kentucky（老龄化桑德斯-布朗中心，肯塔基大学）；Department of Electrical and Computer Engineering, University of Kentucky（电气与计算机工程系，肯塔基大学）；Division of Neuropathology, University of Pittsburgh（神经病理学部，匹兹堡大学）；Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学）；Department of Pathology and Laboratory Medicine, University of California Davis（病理学与实验室医学系，加州大学戴维斯分校）；Department of Laboratory Medicine and Pathology, University of Texas Health（实验室医学与病理学系，德克萨斯大学健康科学中心）
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）
Exploring shared memory architectures for end-to-end gigapixel deep learning
Vanderbilt University（范德堡大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；National Institutes of Health（美国国立卫生研究院）；Vanderbilt University Medical Center（范德堡大学医学中心）；Veterans Affairs Tennessee Valley Healthcare System（退伍军人事务部田纳西谷医疗系统）
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）
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（达纳-法伯癌症研究所）
