Pathology Foundation Models

Latest papers 84

9 papers in the last four weeks, down 18% on the four weeks before. 0.1% of all new papers.

Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
Apr 22, 2026Joakim Nguyen, Jian Yu, Jinrui Fang +7Pathology Foundation Models Medical Image Classification
Dept. of Computer Science University of Texas at Austin Austin, TX, USA · School of Information University of Texas at Austin Austin, TX, USA · Dept. of Computer Science UNC at Chapel Hill Chapel Hill, NC, USA +3
Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
Apr 21, 2026Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +6Pathology Foundation Models Oncology
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA · Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
Apr 17, 2026Enhui Chai, Sicheng Chen, Tianyi Zhang +2Self-Supervised Learning Pathology Foundation Models
School of Computer Science, Northwest University · PuzzleLogic Pte Ltd, Singapore 229594, Singapore · Department of Electrical & Computer Engineering, National University of Singapore +1
Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models
Apr 9, 2026Gexin Huang, Anqi Li, Yusheng Tan +4Computational Pathology Ensemble Learning
University of British Columbia, Vancouver, BC, Canada · Vector Institute, Toronto, ON, Canada · Washington University in St. Louis, St. Louis, MO, USA
MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
Apr 7, 2026Hikmat Khan, Usama Sajjad, Metin N. Gurcan +4Cancer Survival Prediction Computational Pathology
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA · Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
A Generative Foundation Model for Multimodal Histopathology
Apr 4, 2026Jinxi Xiang, Mingjie Li, Siyu Hou +9Computational Pathology Pathology Foundation Models
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA · Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA · Department of Statistics and Data Science, Yale University, New Haven, CT +5
MOOZY: A Patient-First Foundation Model for Computational Pathology
Mar 27, 2026Yousef Kotp, Vincent Quoc-Huy Trinh, Christopher Pal +1Computational Pathology Self-Supervised Pre-Training
CSSE, Concordia University, Montréal, Canada · Mila – Québec AI Institute, Montréal, Canada · CHUM, Université de Montréal, Montréal, Canada +4
LEMON: a foundation model for nuclear morphology in Computational Pathology
Mar 26, 2026Loïc Chadoutaud, Alice Blondel, Hana Feki +3Computational Pathology Pathology Foundation Models
Institut Curie, Paris, France · Mines Paris PSL, Centre for Computational Biology · INSERM U1331, Paris, France +6
GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference Kit
Mar 21, 2026Chad Vanderbilt, Gabriele Campanella, Siddharth Singi +10Computational Pathology Medical Image Benchmarks
Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA · Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Multi-Stage Fine-Tuning of Pathology Foundation Models with Head-Diverse Ensembling for White Blood Cell Classification
Mar 20, 2026Antony Gitau, Martin Paulson, Bjørn-Jostein Singstad +3Fine-Grained Image Classification Pathology Foundation Models
University of South-Eastern Norway · Vestfold Hospital Trust
CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report
Mar 18, 2026Thomas Duboudin, Xavier Fontaine, Etienne Andrier +7Medical Image Synthesis Pathology Foundation Models
Owkin, Inc
A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images
Mar 16, 2026Harishwar Reddy Kasireddy, Patricio S. La Rosa, Akshita Gupta +12Pathology Foundation Models Computational Pathology
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA · Seed Production Innovation, Crop Science Division, Bayer Company, St. Louis, MO, USA · Division of Medicine – Quantitative Health, University of Florida, Gainesville, FL, USA +9
MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models
Mar 9, 2026Minsoo Lee, Jonghyun Kim, Juseung Yun +2Pathology Foundation Models Gene Expression Prediction from Histopathology Images
LG AI Research
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
Mar 6, 2026Qian Da, Yijiang Chen, Min Ju +25Computational Pathology Pathology Foundation Models
Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China · Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA · Department of Basic Education, Qingdao City University, Qingdao, Shandong, China +22
DeNuC: Decoupling Nuclei Detection and Classification in Histopathology
Mar 4, 2026Zijiang Yang, Chen Kuang, Dongmei FuMedical Image Analysis Pathology Foundation Models
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China · Beijing Engineering Research Center of Industrial Spectrum Imaging
Revisiting the Role of Foundation Models in Cell-Level Histopathological Image Analysis under Small-Patch Constraints -- Effects of Training Data Scale and Blur Perturbations on CNNs and Vision Transformers
Mar 4, 2026Hiroki Kagiyama, Toru Nagasaka, Yukari Adachi +5Histopathology Image Classification Computational Pathology
Division of Gastrointestinal Surgery, Department of Surgery, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku,, Kobe 650-0017, Hyogo, Japan · Association of Medical Artificial Intelligence Curation, 505, Sakae Members Office Building, 4-16-8 Sakae, Naka-ku, Nagoya 460-0008, Aichi, Japan · Center for Medical Education and Clinical Training, Kindai University Faculty of Medicine, 1-14-1 Miharadai, Minami-ku, Sakai 590-0111, Osaka, Japan +1
BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology
Mar 3, 2026Xiaojing Guo, Jiatai Lin, Yumian Jia +39Breast Cancer Healthcare
GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights
Mar 3, 2026Qiming He, Jing Li, Tian Guan +26Pathology Foundation Models Histopathology Image Classification
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou, China · Medical Optical Technology R&D Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China +6
Enabling clinical use of foundation models for computational pathology
Feb 25, 2026Audun L Henriksen, Ole-Johan Skrede, Lisa van der Schee +31Computational Pathology Pathology Foundation Models
Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway · Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands · Department of Oncology, University of Oxford, Oxford, UK +20
Mixed Magnification Aggregation for Generalizable Region-Level Representations in Computational Pathology
Feb 25, 2026Eric Zimmermann, Julian Viret, Michal Zelechowski +7Computational Pathology Medical Image Representation Learning
Microsoft Research, Cambridge, MA, United States · Paige, NYC, NY, United States
CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis
Feb 25, 2026Di Zhang, Zhangpeng Gong, Xiaobo Pang +14Pathology Foundation Models Whole-Slide Image Analysis
Xi’an Jiaotong University · University of Cambridge · KingMed +2
Benchmarking Computational Pathology Foundation Models For Semantic Segmentation
Feb 21, 2026Lavish Ramchandani, Aashay Tinaikar, Dev Kumar Das +2Histopathology Image Segmentation Pathology Foundation Models
Aira Matrix Private Limited, Mumbai, India
Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
Nov 7, 2025Jingsong Liu, Han Li, Zhengyang Xu +19Computational Pathology Healthcare
Institute of Pathology, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Munich Data Science Institute (MDSI), Munich, Germany +6
Streamline pathology foundation model by cross-magnification distillation
Sep 27, 2025Ziyu Su, Abdul Rehman Akbar, Usama Sajjad +2Pathology Foundation Models Computational Pathology
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH,USA

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Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images

Joakim Nguyen, Jian Yu, Jinrui Fang, Nicholas Konz+6

Dept. of Computer Science University of Texas at Austin Austin, TX, USA · School of Information University of Texas at Austin Austin, TX, USA · Dept. of Computer Science UNC at Chapel Hill Chapel Hill, NC, USA+3

Pathology Foundation Models Medical Image Classification Histopathology Image Classification Brain Tumor Classification Supervised Contrastive Learning Multiple Instance Learning

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Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic overlap across diagnostically distinct subtypes. While pathology foundation models have advanced patch-level representation learning, their effective adaptation to weakly supervised pediatric brain tumor classification under limited data remains underexplored. In this work, we introduce an expert-guided contrastive fine-tuning framework for pediatric brain tumor diagnosis from whole-slide images (WSI). Our approach integrates contrastive learning into slide-level multiple instance learning (MIL) to explicitly regularize the geometry of slide-level representations during downstream fine-tuning. We propose both a general supervised contrastive setting and an expert-guided variant that incorporates clinically informed hard negatives targeting diagnostically confusable subtypes. Through comprehensive experiments on pediatric brain tumor WSI classification under realistic low-sample and class-imbalanced conditions, we demonstrate that contrastive fine-tuning yields measurable improvements in fine-grained diagnostic distinctions. Our experimental analyses reveal complementary strengths across different contrastive strategies, with expert-guided hard negatives promoting more compact intra-class representations and improved inter-class separation. This work highlights the importance of explicitly shaping slide-level representations for robust fine-grained classification in data-scarce pediatric pathology settings.