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arXiv 2502.10536cs.CVcs.AIcs.LG
PolyPath：为多切片病理报告生成适配大型多模态模型
PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation
Google Research（谷歌研究院）
Faruk Ahmed, Lin Yang, Tiam Jaroensri, Andrew Sellergren, Yossi Matias, Avinatan Hassidim, Greg S. Corrado, Dale R. Webster, Shravya Shetty, Shruthi Prabhakara,… 展开作者
Faruk Ahmed, Lin Yang, Tiam Jaroensri, Andrew Sellergren, Yossi Matias, Avinatan Hassidim, Greg S. Corrado, Dale R. Webster, Shravya Shetty, Shruthi Prabhakara, Yun Liu, Daniel Golden, Ellery Wulczyn, David F. Steiner
The interpretation of histopathology cases underlies many important diagnostic and treatment decisions in medicine. Notably, this process typically requires pathologists to integrate and summarize findings across multiple slides per case. Existing vision-language capabilities in computational pathology have so far been largely limited to small regions of interest, larger regions at low magnification, or single whole-slide images (WSIs). This limits interpretation of findings that span multiple high-magnification regions across multiple WSIs. By making use of Gemini 1.5 Flash, a large multimodal model (LMM) with a 1-million token context window, we demonstrate the ability to generate bottom-line diagnoses from up to 40,000 768x768 pixel image patches from multiple WSIs at 10X magnification. This is the equivalent of up to 11 hours of video at 1 fps. Expert pathologist evaluations demonstrate that the generated report text is clinically accurate and equivalent to or preferred over the original reporting for 68% (95% CI: [60%, 76%]) of multi-slide examples with up to 5 slides. While performance decreased for examples with 6 or more slides, this study demonstrates the promise of leveraging the long-context capabilities of modern LMMs for the uniquely challenging task of medical report generation where each case can contain thousands of image patches.
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）
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）
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）
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）
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Microsoft Research（微软研究院）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Providence Genomics（普罗维登斯基因组学公司）；Earle A. Chiles Research Institute, Providence Cancer Institute（普罗维登斯癌症研究所厄尔·A·奇尔斯研究所）；Providence Research Network（普罗维登斯研究网络）
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）
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）
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）
A Survey of Pathology Foundation Model: Progress and Future Directions
Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Computer Science and Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology（香港理工大学计算机科学与工程系和化学及生物工程系）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李科钦医学院）
MOOZY: A Patient-First Foundation Model for Computational Pathology
CSSE, Concordia University, Montr\' e al, Canada Mila -- Qu\' e bec AI Institute, Montr\' e al, Canada CHUM, Universit\' e de Montr\' e al, Montr\' e al, Canada IRIC, Universit\' e de Montr\' e al, Montr\' e al, Canada Universit\' e de Montr\' e al, Montr\' e al, Canada Canada CIFAR Chair, Polytechnique Montr\' e al, Montr\' e al, Canada Dept.\ of Pathology, McGill University, Montr\' e al, Canada
Foundation Models for Slide-level Cancer Subtyping in Digital Pathology
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech)（以人为本技术大学研究院（HUMAN-Tech））；Universitat Politécnica de Valencia（瓦伦西亚理工大学）；Valencian Graduate School and Research Network for Artificial Intelligence (valgraAI)（瓦伦西亚人工智能研究生院与研究网络（valgraAI））
From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology
Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨工业大学）；University of Maribor（马里博尔大学）；Masaryk University（马萨里克大学）；Masaryk Memorial Cancer Institute（马萨里克纪念癌症研究所）；University of Natural Resources and Life Sciences Vienna（维也纳自然资源与生命科学大学）；BBMRI-ERIC（欧洲生物样本库与生物分子资源研究基础设施联盟）；University Hospital RWTH Aachen（亚琛工业大学医院）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；EMPAIA International e.V.（EMPAIA国际协会）；European Society of Digital and Integrative Pathology（欧洲数字与整合病理学会）
Semantic Mosaicing of Histo-Pathology Image Fragments using Visual Foundation Models
Computational Imaging Research Lab（计算成像研究实验室）；Division of General and Pediatric Radiology（普通放射科与小儿放射科 division）；Christian Doppler Laboratory for Machine Learning Driven Precision Imaging（Christian Doppler 机器学习驱动的精准成像实验室）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Department of Pathology（病理学系）
