Foundation Models (FMs) have recently redefined the state-of-the-art in histopathology by providing robust representations for whole-slide image (WSI) analysis. However, selecting the optimal foundation model (FM) for a specific clinical cohort currently requires multiple preprocessing steps, followed by computationally expensive feature extraction and the training of a Multiple Instance Learning (MIL) aggregator for every model. In this work, we investigate whether efficient tile-level linear probing can serve as a reliable proxy for slide-level performance, reducing the need to run full slide-level pipelines for every candidate encoder. We benchmark 19 state-of-the-art FMs on 42 slide-level and 16 tile-level tasks, comparing tile probing metrics against slide-level outcomes using ABMIL and Mean Pooling aggregations. We observe a high correlation between tile and slide performance across varying task difficulties, indicating that encoder representation quality is the primary determinant of WSI success. Sensitivity analyses show that transferability is stable across models and is more influenced by cohort sizes and numbers of tiles per slide than by average task difficulty. We also measure the agreement in best performing models between tile and slide-level tasks, showing tile benchmarks reliably shortlist strong candidates. Overall, our study indicates that tile-level benchmarking provides an efficient and practical first step for narrowing down candidate models, while slide-level evaluation remains essential for final validation on clinical tasks.
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）
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）
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）
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 Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；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）；Technical University of Munich（慕尼黑工业大学）；University Hospital Aachen（亚琛大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University of Leeds（利兹大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）；Technische Universität Dresden（德累斯顿工业大学）
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）
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）
Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation
School of Computer Science and Enginecring, Central South University（计算机科学与工程学院，中南大学）；School of Computer Science（计算机科学学院）；Enginecring, Central South University（工程学院，中南大学）
The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population
Koya University（科亚大学）；Karolinska Institutet（卡罗林斯卡学院）；Hawler Medical University（哈勒医科大学）；PAR Private Hospital（PAR私立医院）；University of Sulaimani（苏莱曼尼亚大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）
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）
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）
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Dept. of Computer Science, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；AI Center, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院人工智能中心）；Signal Processing Laboratory (LTS4), EPFL, Lausanne, Switzerland（日内瓦联邦理工学院信号处理实验室）；University of Basel, Basel, Switzerland（巴塞尔大学）；University Hospital of Basel, Basel, Switzerland（巴塞尔大学医院）；Idiap Research Institute, Martigny, Switzerland（伊迪普研究 institute）；Icahn School of Medicine at Mount Sinai, New York, United States（辛辛那提医学中心伊坎医学院）
Deep Learning-Based Fixation Type Prediction for Quality Assurance in Digital Pathology
Institute of Pathology, Technical University of Munich, Germany（慕尼黑技术大学病理学研究所）；School of Computation, Information and Technology, Technical University of Munich, Germany（慕尼黑技术大学计算、信息与技术学院）；Munich Center for Machine Learning, Germany（慕尼黑机器学习中心）；Institute of Pathology, University of Regensburg, Regensburg, Germany（罗森堡大学病理学研究所）；Institute of Pathology and Molecular Diagnostics, University Hospital of Augsburg, Germany（埃格利希大学医院病理学与分子诊断研究所）；Digital Medicine, University Hospital of Augsburg, Germany（埃格利希大学医院数字医学）；Bavarian Cancer Research Center (BZKF), Augsburg, Germany（巴伐利亚癌症研究中心（BZKF））；Bio M {}^{\text{M}} Biotech Cluster Development GmbH, Martinsried, Germany（Bio M {}^{\text{M}} 生物技术集群发展 GmbH）；Institute of Pathology, Medical Faculty Mannheim, University Heidelberg, Germany（海德堡大学曼海姆医学学院病理学研究所）；Munich Data Science Institute, Technical University of Munich, Germany（慕尼黑数据科学研究所）
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）
