Pathology Foundation Models

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

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LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models
Jul 13, 2026Gangsu Kim, Won-Ki JeongComputational Pathology Pathology Foundation Models
Department of Computer Science and Engineering, College of Informatics, Korea University, Republic of Korea
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
Jul 10, 2026Jiawen Li, Tian Guan, Huijuan Shi +5Computational Pathology Unified Multimodal Models
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China · Department of Engineering Science, University of Oxford, Oxford, UK +1
Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models
Jul 6, 2026Tim Lenz, Maurice Heide, Marco Gustav +2Computational Pathology Pathology Foundation Models
EKFZ for Digital Health, TU Dresden, Dresden, Germany · Pathology, University of Augsburg, Augsburg, Germany · Bavarian Cancer Research Center (BZKF), Augsburg, Germany +3
The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models
Jul 5, 2026Dhyey Yajnik, Amina Asif, Fayyaz MinhasDistribution Shift Robustness Neural Network Robustness
Predictive Systems in Biomedicine Lab, Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, United Kingdom
Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
Jun 29, 2026Gbègninougbo Aurel Davy Tchokponhoue, Sevda Öğüt, Ali Idri +2Pathology Foundation Models Deep Ensembles
EPFL, Lausanne, Switzerland · UM5, Rabat, Morocco
Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction
Jun 29, 2026Dominik Winter, Dominik Vonficht, Loïc Le Bescond +6Computational Pathology Cross-Modal Alignment
AstraZeneca Computational Pathology and Biomarkers, Munich, Germany · AstraZeneca, Early Oncology and Translation Medicine, Cambridge, UK
Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
Jun 27, 2026Yishu Zhang, Shushan Wu, Zhenzhong Zhang +8Computational Pathology Pathology Foundation Models
University of North Carolina at Chapel Hill
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
Jun 16, 2026Tianyu Liu, Ziqing Wang, Zhaokang Liang +12Pathology Foundation Models Clinical Prediction
Program of Computational Biology and Bioinforamtics, Yale University, USA · Broad Institute of MIT and Harvard, USA · These authors contributed equally to this work. +12
SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
Jun 16, 2026Wan Siti Halimatul Munirah Wan Ahmad, Faris Syahmi Samidi, Mohammad Badal Ahmmed +3Computational Pathology Medical Report Generation
Department of Data Science and Artificial Intelligence, School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Malaysia · Department of Electrical and Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, Malaysia · Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, Malaysia
Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis
Jun 15, 2026Jingyu Hu, Giuseppe Tripodi, Reed Naidoo +2Computational Pathology Multimodal Robustness
The Alan Turing Institute, London, United Kingdom · University of Manchester, Manchester, United Kingdom · The Institute of Cancer Research, London, United Kingdom +1
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Jun 10, 2026Kai Standvoss, Miriam Hägele, Rosemarie Krupar +25Computational Pathology Pathology Foundation Models
Aignostics, Germany · Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany · Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Germany +9
From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology
Jun 9, 2026Sofiène Boutaj, Leo Fillioux, Maria Vakalopoulou +2Pathology Foundation Models Medical Image Benchmarks
Université Paris-Saclay, CentraleSupélec, Gustave Roussy, INSERM, IHU PRISM, Cancer Data Science Unit, France · Université Paris-Saclay, CentraleSupélec, MICS Laboratory, France
DaX: Learning General Pathology Representations Across Scales
Jun 5, 2026Bokai Zhao, Yiyang Zhang, Long Bai +3Pathology Foundation Models Medical Image Representation Learning
DAMO Academy, Alibaba Group · Institute of Automation, Chinese Academy of Sciences · Hupan Lab
A Pathology Foundation Model for Gastric Cancer with Real-World Validation
Jun 3, 2026Ling Liang, Jiabo Ma, Zhengyu Zhang +25Computational Pathology Medical Diagnosis
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +18
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Jun 3, 2026Dilakshan Srikanthan, Amoon Jamzad, Paul Wilson +5Pathology Foundation Models Explainable Medical Image Analysis
Translational Medicine, School of Medicine, Queen’s University, Kingston, ON, Canada · School of Computing, Queen’s University, Kingston, ON, Canada · Department of Surgery, Queen’s University, Kingston, ON, Canada
Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model
May 29, 2026Fengtao Zhou, Yingxue Xu, Zhengyu Zhang +20Spatial Transcriptomics Cross-Modal Alignment
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +16
When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
May 29, 2026Dylan Steiner, Gustavo Arango-Argoty, Gerald Sun +1Pathology Foundation Models Multimodal Embedding
Oncology Data Science & AI, R&D, AstraZeneca, Boston, US
SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
May 28, 2026Mingyi He, Xinyi Guo, Xitong Ling +7Self-Supervised Pre-Training Pathology Foundation Models
Beijing University of Chemical Technology · South China Normal University · Tsinghua University
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
May 25, 2026Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23Computational Pathology Clinical Decision Support
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +15
Benchmarking Pathology Foundation Models for Spatial Domain Understanding
May 25, 2026Bokai Zhao, Yiyang Zhang, Yuanchi Zhu +6Pathology Foundation Models Medical Image Benchmarks
School of Artificial Intelligence, University of Chinese Academy of Sciences. · Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. · Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences. +2
Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology
May 24, 2026Ben Vardi, Dana Schonberger, Yuval Friedmann +4Computational Pathology Unsupervised Domain Adaptation
Reichman University, Herzliya, Israel · Institute of Pathology, Sheba Medical Center, Ramat-Gan, Israel · Technion - Israel Institute of Technology, Haifa, Israel
Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
May 8, 2026Weiming Chen, Xitong Ling, Zhenyang Cai +5Computational Pathology Histopathology Image Segmentation
1Tsinghua Shenzhen International Graduate School, Tsinghua University · 3Research Institute of Tsinghua, Pearl River Delta · 2The Chinese University of Hong Kong, ShenZhen
A Breast Vision Pathology Foundation Model for Real-world Clinical Utility
May 6, 2026Yingxue Xu, Zhengyu Zhang, Xiuming Zhang +32Healthcare Clinical Decision Support
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Pathology, Nanfang Hospital, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China · Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China +18
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
May 5, 2026Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53Computational Pathology Healthcare
Biological Spatial Priors Regularize Foundation Model Representations for Cross-Site MSI Generalization in Colorectal Cancer
May 4, 2026Dasari Naga RajuSpatial Generalization Pathology Foundation Models
Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation
May 1, 2026Suryakant Singh, Saarthak Kapse, Joel Saltz +1Computational Pathology Medical Report Generation
Stony Brook University
Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Apr 28, 2026Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly +5Pathology Foundation Models Oncology
KIMIA Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA. · Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. · Department of Computer Science, Temple University, Philadelphia, PA, USA. +2
Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction
Apr 27, 2026Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson +2Cancer Survival Prediction Survival Analysis
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden · Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden · Department of Engineering Science, University of Oxford, Oxford, UK +1
Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models
Apr 26, 2026Adam Kukučka, Ondřej Fabián, Vít Musil +1Computational Pathology Pathology Foundation Models
Masaryk University, Faculty of Informatics, Brno, Czech Republic · Institute of Clinical and Experimental Medicine, Prague, Czech Republic
Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models
Apr 23, 2026Laura Valeria Perez-Herrera, M. J. Garcia-Gonzalez, Karen Lopez-LinaresComputational Pathology Pathology Foundation Models
* Vicomtech Foundation, Basque Research and Technology Alliance (BRTA) · ± eHealth Group, Bioengineering Area, Biogipuzkoa Health Research Institute

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LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models

Gangsu Kim, Won-Ki Jeong

Department of Computer Science and Engineering, College of Informatics, Korea University, Republic of Korea

Computational Pathology Pathology Foundation Models Medical VLMs Multimodal Foundation Models Knowledge Distillation

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Pathology Foundation Models (PFMs) offer powerful Whole Slide Image (WSI) representations but suffer from massive computational costs. While Knowledge Distillation (KD) can create efficient student models, existing multi-teacher methods often use suboptimal uniform weighting that ignores tissue heterogeneity. We propose LaGuadia (Language-Guided Adaptive DistillAtion), a framework that develops a compact pathology image encoder by dynamically integrating expertise from multiple PFMs under clinical linguistic guidance. Our approach utilizes a multi-stage pipeline: first, extracting visually observable clinical keywords from pathology reports; second, aligning visual features with these keywords via a Vision-Language meta-teacher (MedSigLIP) to provide dense semantic guidance; and finally, performing adaptive KD where teacher contributions are weighted based on their semantic alignment with the clinical narrative. Experiments on WSI captioning, visual question answering, and slide-level classification tasks demonstrate that an 87M parameter LaGuadia student model matches or exceeds foundation-scale models such as GigaPath and UNI, achieving strong factual consistency and robust generalization. These results highlight clinical language as an effective semantic anchor for building efficient and reliable digital pathology systems. Code is available at https://github.com/hvcl/LaGuadia.