Enabling clinical use of foundation models for computational pathology
Institute for Cancer Genetics and Informatics, Oslo University Hospital（癌症遗传学与信息学研究所，奥斯陆大学医院）；Department of Pathology, University Medical Center Utrecht（病理学系，乌得勒支大学医学中心）；Department of Oncology, University of Oxford（肿瘤学系，牛津大学）；CRUK Beatson Institute of Cancer Research, Garscube Estate（CRUK贝茨癌症研究中心，加尔斯克里特庄园）；Glasgow Tissue Research Facility, University of Glasgow, Queen Elizabeth University Hospital（格拉斯哥组织研究设施，格拉斯哥大学，伊丽莎白女王大学医院）；Area for Improvement and Digital Transformation, Norwegian Offshore Directorate（改进与数字化转型部门，挪威海上管理局）；Pathology Department, Hospital Clínic, Barcelona, Spain（病理学系，巴塞罗那医院，西班牙）；Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain（August Pi I Sunyer生物医学研究所（IDIBAPS），巴塞罗那，西班牙）；Department of Clinical Foundations, Universitat de Barcelona（临床基础系，巴塞罗那大学）；School of Cancer Sciences, Wolfson Wohl Cancer Research Centre, University of Glasgow（癌症科学学院，沃尔夫森沃尔夫癌症研究中心，格拉斯哥大学）；Institute of Clinical Medicine, University of Oslo（临床医学研究所，奥斯陆大学）；Department of Gastrointestinal Surgery, Oslo University Hospital（胃肠外科系，奥斯陆大学医院）
Audun L Henriksen, Ole-Johan Skrede, Lisa van der Schee, Enric Domingo, Karolina Cyll, Sepp de Raedt, Ilyá Kostolomov, Jennifer Hay, Wanja Kildal, Joakim Kalsne… 展开作者
Audun L Henriksen, Ole-Johan Skrede, Lisa van der Schee, Enric Domingo, Karolina Cyll, Sepp de Raedt, Ilyá Kostolomov, Jennifer Hay, Wanja Kildal, Joakim Kalsnes, Robert W Williams, Manohar Pradhan, John Arne Nesheim, Hanne Askautrud, Maria Isaksen, Karmele Saez de Gordoa, Miriam Cuatrecasas, Joanne Edwards, TransSCOT group, Arild Nesbakken, Neil A Shepherd, Ian Tomlinson, Daniel-Christoph Wagner, Rachel Kerr, Tarjei Sveinsgjerd Hveem, Knut Liestøl, Yoshiaki Nakamura, Marco Novelli, Masaaki Miyo, Sebastian Försch, David N Church, Miangela M Lacle, David J Kerr, Andreas Kleppe
Foundation models for computational pathology are expected to facilitate the development of high-performing, generalisable deep learning systems. However, in addition to biologically relevant features, current foundation models also capture pre-analytic and scanner-specific variation that bias the predictions made by downstream task-specific models trained on these features. Here we show that introducing novel robustness losses during downstream model training reduces sensitivity to technical variability. A purpose-designed comprehensive experimentation setup with 27,042 whole-slide images from 6,155 patients is used to train thousands of models from the features of eight well-known foundation models for computational pathology. In addition to a substantial improvement in robustness, our approach improves classification accuracy by focusing on biologically relevant features. It mitigates robustness limitations of foundation models for computational pathology without retraining the foundation models themselves, enabling development of models that are more suitable in real-world clinical use.
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学生物医药与健康工程研究院，清华大学深圳国际研究生院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院病理科）；Medical Optical Technology R&D Center, Research Institute of Tsinghua, Pearl River Delta（清华珠三角研究院医学光学技术研发中心）
Robustifying pathology foundation models via fine-tuning
Waiv；Institute of Pathology, Technical University of Munich（慕尼黑工业大学病理学研究所）；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）
GAS-MIL: Group-Aggregative Selection Multi-Instance Learning for Ensemble of Foundation Models in Digital Pathology Image Analysis
The University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；Simmons Comprehensive Cancer Center（西蒙斯综合癌症中心）
Artificial Intelligence for Digital and Computational Pathology
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；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-法伯癌症研究所）；Massachusetts Institute of Technology（麻省理工学院）
Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Research Institute of Tsinghua, Pearl River Delta（清华大学 Pearl River Delta 研究院）；The Chinese University of Hong Kong, ShenZhen（香港中文大学（深圳））
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
The Hong Kong University of Science and Technology（香港科技大学）；Southern Medical University（南方医科大学）；Guangdong Provincial Key Laboratory of Molecular Tumor Pathology（广东省分子肿瘤病理学重点实验室）；Jinfeng Laboratory（金凤实验室）；Shandong First Medical University（山东第一医科大学）；Shandong Provincial Qianfoshan Hospital（山东省千佛山医院）；The Third Affiliated Hospital of Kunming Medical University（昆明医科大学第三附属医院）；Yunnan Cancer Hospital（云南省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）；Prince of Wales Hospital（威尔士亲王医院）；Sun Yat-sen University（中山大学）
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
Indiana University School of Medicine（印第安纳大学医学院）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center（印第安纳大学梅尔文与布伦·西蒙综合癌症中心）；University of Warwick（华威大学）；Emory University（埃默里大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Singapore General Hospital（新加坡中央医院）
MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center（俄亥俄州立大学医学学院病理学系，韦克斯纳医学中心）；Center for Artificial Intelligence Research, Wake Forest University School of Medicine（威克森林大学医学院人工智能研究中心）
VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology
Department of Pathology and Laboratory Medicine, University of Pennsylvania（病理学与实验室医学系，宾夕法尼亚大学）；Department of Electrical and System Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（生物医学工程系，佐治亚理工学院和埃默里大学）；NVIDIA Corporation（NVIDIA公司）；Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania（生物统计学、流行病学与信息学系，宾夕法尼亚大学）
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Microsoft Research（微软研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Minnesota（明尼苏达大学）；Stanford University（斯坦福大学）；Duke University（杜克大学）；New York University（纽约大学）
Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA（电气与计算机工程学院，佐治亚理工学院）；Department of Biomedical Engineering, Georgia Institute of Technology（生物医学工程系，佐治亚理工学院）；Emory University, Atlanta, GA, USA（埃默里大学）
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Department of Pathology, Mass General Brigham, Harvard Medical School, Boston, MA, USA（病理学系，马萨诸塞州总医院与哈佛医学院，波士顿，马萨诸塞州，美国）；Department of Computer Science & Technology, University of Cambridge, Cambridge, UK（计算机科学与技术系，剑桥大学，剑桥，英国）；Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA（癌症计划，哈佛与麻省理工联合学院，剑桥，马萨诸塞州，美国）；Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA（数据科学计划，达纳-法伯癌症研究所，波士顿，马萨诸塞州，美国）；Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA（哈佛-麻省理工健康科学与技术 division，麻省理工学院，剑桥，马萨诸塞州，美国）；Télécom Paris, Institut Polytechnique de Paris, Paris, France（巴黎电信学院，巴黎理工学院，巴黎，法国）；Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA（哈佛大学数据科学倡议，哈佛大学，剑桥，马萨诸塞州，美国）
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fourth Military Medical University（第四军医大学）；Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）；SenseTime Research（商汤科技研究院）；Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）；Shengqiang Technology Co. Ltd.（胜强科技有限公司）；Institute of Materials Research, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院材料研究院）
Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；The University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Southern Methodist University（南卫理公会大学）
Benchmarking Pathology Foundation Models for Spatial Domain Understanding
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Brainnetome Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑网膜工程中心）；Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所北京脑网膜与脑机接口重点实验室）；DAMO Academy, Alibaba Group（阿里云达摩院）；ShanghaiTech University（上海科技大学）
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）
Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits
University of Warwick（华威大学）；University of Birmingham（伯明翰大学）；University Hospitals Coventry & Warwickshire（考文垂和沃里克郡大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings
Technical University of Munich（慕尼黑技术大学）；Helmholtz Munich（马克斯·普朗克研究所慕尼黑分部）；Munich Data Science Institute（慕尼黑数据科学研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
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）
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）
LEMON: a foundation model for nuclear morphology in Computational Pathology
Institut Curie（居里研究所）；Mines Paris PSL, Centre for Computational Biology (CBIO)（巴黎高科矿业学院计算生物学中心）；INSERM, U1331（法国国家健康与医学研究院U1331）；Institut Curie, U1353/UMR9029 IRIS, Equipe IMPACT（居里研究所U1353/UMR9029 IRIS IMPACT团队）
Unifying Multiple Foundation Models for Advanced Computational Pathology
Shanghai Jiao Tong University（上海交通大学）；Washington University in St. Louis（华盛顿大学）；University of Science and Technology Beijing（北京科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumours, Ministry of Education, The First Hospital of China Medical University（外科肿瘤科和普通外科，国家教育委员会胃肠道肿瘤精准诊断与治疗重点实验室，中国医科大学第一医院）；Shanghai Innovation Institute（上海创新研究院）；Sensetime Research（商汤科技研究院）
Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact
Prince Sattam bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Warwick（华威大学）；Frontier AI（前沿人工智能）
Ensemble learning of pathology foundation models for precision oncology
Department of Radiation Oncology, Stanford University School of Medicine（放射肿瘤科，斯坦福大学医学院）；Department of Pathology, Stanford University School of Medicine（病理学系，斯坦福大学医学院）；Department of Medicine (Oncology), Stanford University School of Medicine（医学系（肿瘤学），斯坦福大学医学院）；Department of Neurosurgery, Stanford University School of Medicine（神经外科，斯坦福大学医学院）；Stanford Institute for Human-Centered Artificial Intelligence（斯坦福大学人本人工智能研究所）
