Isaiah Lahr, Saghir Alfasly, Peyman Nejat, Jibran Khan, Luke Kottom, Vaishnavi Kumbhar, Areej Alsaafin, Abubakr Shafique, Sobhan Hemati, Ghazal Alabtah, Nneka Comfere, Dennis Murphee, Aaron Mangold, Saba Yasir, Chady Meroueh, Lisa Boardman, Vijay H. Shah, Joaquin J. Garcia, H. R. Tizhoosh
在组织学和组织病理学图像档案中搜索相似图像是一项关键任务，可在患者匹配中发挥作用，其用途涵盖分诊、诊断、预后和预测等多个方面。全切片图像（WSIs）是安装在载玻片上的组织标本的高精细数字表示。将 WSI 与 WSI 进行匹配可作为患者匹配的关键方法。本文报告了对四种搜索方法的广泛分析与验证，包括视觉词袋（BoVW）、Yottixel、SISH、RetCCL 及其若干潜在变体。我们分析了它们的算法和结构，并评估了其性能。在此评估中，我们使用了四个内部数据集（1269 名患者）和三个公开数据集（1207 名患者），共计超过 200,000 个图像块，来自五个主要部位的 38 个不同类别/亚型。某些搜索引擎（例如 BoVW）表现出显著的效率和速度，但准确率较低。相反，Yottixel 等搜索引擎在效率和速度方面表现良好，并能提供中等准确的结果。近期提出的方法（包括 SISH）表现出效率低下且结果不一致，而 RetCCL 等替代方案在准确性和效率方面均显不足。进一步的研究对于解决组织病理学图像搜索中准确性与最小存储需求的双重问题至关重要。
英文摘要：
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient matching. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets ($1269$ patients) and three public datasets ($1207$ patients), totaling more than $200,000$ patches from $38$ different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.
A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification
University of Leeds（利兹大学）；Leeds Institute of Medical Research at St James's（圣詹姆斯利兹医学研究所）；Leeds Cancer Centre（利兹癌症中心）；St James's University Hospital（圣詹姆斯大学医院）；Leeds Teaching Hospitals NHS Trust（利兹教学医院国民保健信托基金）；National Pathology Imaging Cooperative (NPIC)（国家病理影像合作组织）
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心）；School of Computing, University of Leeds（利兹大学计算机学院）；Leeds Institute of Medical Research at St James’s, School of Medicine, University of Leeds（利兹大学医学院圣詹姆斯利兹医学研究所）；Leeds Cancer Centre, St James’s University Hospital（圣詹姆斯大学医院利兹癌症中心）
Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
University of Chicago Medical Center（芝加哥大学医学中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；DV Group, LLC（DV集团有限责任公司）；University of Chicago（芝加哥大学）；Mayo Clinic（梅奥诊所）；University of Wisconsin at Madison（威斯康星大学麦迪逊分校）
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
Hunan University（湖南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Central South University（中南大学）；Hunan Clinical Medical Research Center for Cancer Pathogenic Genes Testing and Diagnosis（湖南省肿瘤致病基因检测与诊断临床医学研究中心）；Shanghai Cancer Institute（上海市肿瘤研究所）；Renji Hospital, School of Medicine, Shanghai Jiaotong University（上海交通大学医学院附属仁济医院）；Shanghai Jiaotong University（上海交通大学）
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence
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）
Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading
Dartmouth College（达特茅斯学院）；Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Massachusetts General Hospital（麻省总医院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希契科克医疗中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）
Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Ordensklinikum Linz（林茨教团医院）；University of Alberta（阿尔伯塔大学）；Exact Imaging（Exact Imaging公司）
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Hannover Medical School（汉诺威医学院）；Medical Center - University of Freiburg（弗莱堡大学医学中心）；RWTH Aachen University Hospital（亚琛工业大学医院）；Klinikum Bremen-Mitte（不来梅中部医院）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所）；Bremen Cancer Registry（不来梅癌症登记处）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所IOSB）；German Heart Center Berlin（柏林德国心脏中心）
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
McMaster University（麦克马斯特大学）；Mayo Clinic（梅奥诊所）；William Osler Health System（威廉·奥斯勒医疗系统）
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
Visvesvaraya National Institute of Technology（维斯瓦拉亚国家理工学院）；AIIMS Nagpur（那格浦尔全印度医学科学研究所）；Indian Institute of Technology Mandi（曼迪印度理工学院）；Universitat de Barcelona（巴塞罗那大学）
Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology
Technion - IIT（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）
DA-SSL: self-supervised domain adaptor to leverage foundational models in turbt histopathology slides
National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）；Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心）；University of California, San Francisco（加州大学旧金山分校）；Yale University（耶鲁大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Washington（华盛顿大学）
A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images
Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）；Seed Production Innovation, Crop Science Division, Bayer Company（拜耳公司种子生产创新部）；Division of Medicine – Quantitative Health, University of Florida（佛罗里达大学医学部-定量健康分部）；Department of Health Outcomes and Biomedical Informatics, University of Florida（佛罗里达大学健康结果与生物医学信息学系）；Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine（佛罗里达大学医学院病理学、免疫学与实验室医学系）；Kidney Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health（美国国立卫生研究院糖尿病、消化系统与肾病研究所肾病分支）；Indiana University School of Medicine（印第安纳大学医学院）；Departments of Medicine, Washington University School of Medicine（华盛顿大学医学院医学部）；Universidade de Coimbra（科英布拉大学）；Department of Pathology and Laboratory Medicine, University of California at Davis School of Medicine（加州大学戴维斯分校医学院病理学与实验室医学系）；Department of Pathology, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院病理学系）
FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification
International Islamic University Chittagong (IIUC)（吉大港国际伊斯兰大学）；American International University-Bangladesh (AIUB)（美国国际大学孟加拉分校）；King Saud University（沙特国王大学）；Charles Sturt University（查尔斯特大学）
A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection
Austrian Center for Medical Innovation and Technology（奥地利医学创新与技术中心）；Danube Private University（多瑙河私立大学）；Medical University of Vienna（维也纳医科大学）；Patho im Zentrum（中心病理机构）；Institute for Systems and Robotics (ISR-Lisboa/LARSyS)（系统与机器人研究所（里斯本系统与机器人研究所/卢西塔尼亚机器人与系统协会））；Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting
Mars Digital Technologies（玛氏数字科技）；Antech Diagnostics（Antech诊断公司）；Stanford University（斯坦福大学）
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Broad Institute of Harvard and MIT（哈佛大学与麻省理工学院博德研究所）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）
Towards Fully Automated Medical Imaging Code Generation via Validation-based Context Engineering
University of Auckland（奥克兰大学）；Griffith University（格里菲斯大学）；Suzhou Industrial Park Monash Research Institute of Science and Technology（苏州工业园区莫纳什科技研究院）；Beijing Institute of Technology（北京理工大学）；National University of Singapore（新加坡国立大学）
SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
Department of Data Science and Artificial Intelligence, School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University（双威大学工程与技术学院计算与人工智能学院数据科学与人工智能系）；Faculty of Dentistry, Universiti Malaya（马来亚大学牙科学院）
Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
Stony Brook University（石溪大学）；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）；Fraunhofer Institute for Medical Image Computing MEVIS（弗劳恩霍夫医学图像计算研究所）；University of Bremen（不来梅大学）
Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohort
Koya University（科亚大学）；Karolinska Institutet（卡罗林斯卡学院）；Hawler Medical University（霍勒医科大学）；PAR Private Hospital（PAR私立医院）；University of Sulaimani（苏莱曼尼大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）
GAFR-Net: A Graph Attention and Fuzzy-Rule Network for Interpretable Breast Cancer Image Classification
1 Department of IT Engineering, Mokwon University, Daejeon 35349, South Korea；2 School of Digital Arts, Jiangxi Arts \& Ceramics Technology Institute, Jingdezhen 333001, China
