Molecular-driven Foundation Model for Oncologic Pathology
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Cancer Program, Broad Institute of Harvard and MIT（哈佛与麻省理工学院博德研究所癌症项目）；Health Sciences and Technology, Harvard-MIT（哈佛-麻省理工健康科学与技术部）；Harvard 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, TUM（慕尼黑工业大学计算、信息与技术学院）；Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)（麻省理工学院电气工程与计算机科学系）；Sydney Precision Data Science Center, The University of Sydney（悉尼大学悉尼精准数据科学中心）；Department of Oncologic Pathology, Dana-Farber Cancer Institute（达纳-法伯癌症研究所肿瘤病理科）；Department of Pathology, Boston Children’s Hospital（波士顿儿童医院病理科）；Harvard Data Science Initiative, Harvard University（哈佛大学数据科学倡议）
Anurag Vaidya, Andrew Zhang, Guillaume Jaume, Andrew H. Song, Tong Ding, Sophia J. Wagner, Ming Y. Lu, Paul Doucet, Harry Robertson, Cristina Almagro-Perez, Ric… 展开作者
Anurag Vaidya, Andrew Zhang, Guillaume Jaume, Andrew H. Song, Tong Ding, Sophia J. Wagner, Ming Y. Lu, Paul Doucet, Harry Robertson, Cristina Almagro-Perez, Richard J. Chen, Dina ElHarouni, Georges Ayoub, Connor Bossi, Keith L. Ligon, Georg Gerber, Long Phi Le, Faisal Mahmood
Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles - the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables Threads to capture the tissue's underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction, and survival prediction, Threads outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community.
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）
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT
Biomaps（生物图谱研究所）；INSERM（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）；CEA（法国原子能和替代能源委员会）；Université Paris-Saclay（巴黎萨克雷大学）；Guerbet Research（Guerbet 研究中心）；Gustave Roussy Institute（古斯塔夫·鲁西研究所）；Pellegrin University Hospital（佩勒格林大学医院）；Bordeaux Institute of Oncology（波尔多肿瘤研究所）
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）
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））
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）
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）
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）
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）
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）
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）
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）
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）
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）
Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
Institute of Pathology, Technical University of Munich（慕尼黑技术大学病理学研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Computer Aided Medical Procedures (CAMP), Technical University of Munich（慕尼黑技术大学计算机辅助医疗程序中心）；School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology（大连理工大学医学院生物医学工程学院）；Affiliated Hospital of Chifeng University（赤峰大学附属医院）；Center for Medical Imaging, Robotics, and Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, China（苏州先进研究院医学影像、机器人与分析计算与学习中心）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；The First Hospital and the College of Basic Medical Sciences of China Medical University（中国医科大学第一医院及基础医学科学学院）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
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）
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Flensburg University of Applied Sciences（弗伦斯堡应用科学大学）；University of Veterinary Medicine, Vienna（维也纳兽医大学）；Freie Universität Berlin（柏林自由大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Medical University of Vienna（维也纳医科大学）；Julius-Maximilians-Universität Würzburg（维尔zburg大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Towards a Visual-Language Foundation Model for Computational Pathology
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Harvard-MIT（哈佛-麻省理工学院）；Harvard 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）；Wexner Medical Center, Ohio State University（俄亥俄州立大学韦克斯纳医学中心）；Harvard Data Science Initiative, Harvard University（哈佛大学数据科学计划）
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）
A multimodal vision foundation model for generalizable knee pathology
Beihang University（北京航空航天大学）；Peking University Third Hospital（北京大学第三医院）；Institute of Sports Medicine of Peking University（北京大学运动医学研究所）；Ministry of Education（教育部）
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）
HistoEncoder: a digital pathology foundation model for prostate cancer
University of Helsinki（赫尔辛基大学）；Helsinki University Hospital（赫尔辛基大学医院）；Skåne University Hospital（斯科讷大学医院）；Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki（赫尔辛基大学HiLIFE项目下属芬兰分子医学研究所（FIMM））；iCAN Digital Precision Cancer Medicine Flagship（iCAN数字精准癌症医学旗舰项目）
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
Mayo Clinic（梅奥诊所）；Charité（夏里特医学院）；Aignostics（艾格诺斯蒂克斯公司）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max-Planck Institute for Informatics（马克斯·普朗克信息学研究所）；German Cancer Research Center（德国癌症研究中心）
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）
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；University of Washington（华盛顿大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；Medical Optical Technology R&D Center, Research Institute of Tsinghua（清华研究院医学光学技术研发中心）；Jinfeng Laboratory（金凤实验室）
Prompt-Guided Foundation Model Tuning for Pathology Image Classification
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology（香港科技大学化学与生物工程系）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology（香港科技大学神经系统疾病国家重点实验室）
