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arXiv 2202.07001eess.IVcs.CV
Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images
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）
Quoc Dang Vu, Kashif Rajpoot, Shan E Ahmed Raza, Nasir Rajpoot
Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can now be carried out based on analysis of multi-gigapixel tissue images, also known as whole-slide images (WSIs). Recently, deep convolutional neural networks (CNNs) have been proposed to derive unsupervised WSI representations; these are attractive as they rely less on expert annotation which is cumbersome. However, a major trade-off is that higher predictive power generally comes at the cost of interpretability, posing a challenge to their clinical use where transparency in decision-making is generally expected. To address this challenge, we present a handcrafted framework based on deep CNN for constructing holistic WSI-level representations. Building on recent findings about the internal working of the Transformer in the domain of natural language processing, we break down its processes and handcraft them into a more transparent framework that we term as the Handcrafted Histological Transformer or H2T. Based on our experiments involving various datasets consisting of a total of 5,306 WSIs, the results demonstrate that H2T based holistic WSI-level representations offer competitive performance compared to recent state-of-the-art methods and can be readily utilized for various downstream analysis tasks. Finally, our results demonstrate that the H2T framework can be up to 14 times faster than the Transformer models.
EndoNet: model for automatic calculation of H-score on histological slides
Research Institute of Molecular and Cellular Medicine, Peoples’ Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学分子与细胞医学研究所）；A.P. Avtsyn Scientific Research Institute of Human Morphology（阿夫钦人类形态学科学研究所）
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Washington State University（华盛顿州立大学）；Hanoi Medical University（河内医科大学）；VinUniversity（文森大学）；Hanoi University of Science and Technology（河内科技大学）
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
Université Paris Cité（巴黎西岱大学）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；French University Institute (IUF)（法国大学研究院）
针对肝细胞癌 WSI 全肿瘤分割难题，提出分层 Transformer 架构 HiTrans，在 4096×4096 图像块上学习全局依赖并提升分割性能。
University of California, Davis（加州大学戴维斯分校）；University of San Francisco Data Institute（旧金山大学数据研究所）；Wicklow AI in Medicine Research Initiative（威克洛医学人工智能研究计划）；University of Texas, San Antonio Health（德克萨斯大学圣安东尼奥健康分校）
Stanford School of Medicine（斯坦福医学院）；Center for Artificial Intelligence in Medicine & Imaging（医学与人工智能中心）；Department of Urology（泌尿外科）；Institute for Pathology and Cytology, Schüttorf（许特霍夫病理与细胞研究所）；Department of Radiology（放射科）；Department of Biomedical Data Science（生物医学数据科学系）
IBM Research Europe（IBM欧洲研究院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛-麻省理工博德研究所）；Dana-Farber/Harvard Cancer Center（丹娜-法伯/哈佛癌症中心）；ETH Zurich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；Uppsala University（乌普萨拉大学）
UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift
Ummon HealthTech SAS；Centre Georges-François Leclerc（乔治-弗朗索瓦·勒克莱尔中心）；ICMUB laboratory, Université de Bourgogne Europe（勃艮第欧洲大学ICMUB实验室）；University Hospital of Dijon（第戎大学医院）；Hospital de Tortosa Verge de la Cinta (HTVC), Catalan Health Institute (ICS)（托尔托萨维尔赫-德拉辛塔医院 (HTVC)，加泰罗尼亚卫生研究所 (ICS)）；Pere Virgili Institute for Health Research (IISPV)（佩雷·维尔吉利健康研究所 (IISPV)）；Universitat Rovira i Virgili (URV)（罗维拉-威尔吉利大学 (URV)）；Nałęcz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences（波兰科学院纳文奇生物控制论与生物医学工程研究所）；Cairo University（开罗大学）；University of Udine（乌迪内大学）
CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns
University of Warwick（华威大学）；King Saud University（沙特国王大学）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂和华威大学医院NHS信托基金）；Histofy Ltd（Histofy有限公司）
ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis
USTC（中国科学技术大学）；NUDT（南京理工大学）；SCNU（华南师范大学）；NTU（南洋理工大学）；Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing（安徽省生物医学成像与智能处理重点实验室）
Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
Nanjing University of Information Science and Technology（南京信息工程大学）；Case Western Reserve University（凯斯西储大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Nanjing Women and Children's Healthcare Hospital（南京市妇幼保健院）；Guangdong Provincial People's Hospital（广东省人民医院）；Nanjing University of Science and Technology（南京理工大学）；Lishui Hospital of Zhejiang University（浙江大学丽水医院）
