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Towards a general-purpose foundation model for computational pathology
Open accessAbstract
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology. Pretrained using over 100,000 diagnostic histopathological slides across 20 major tissue types, a self-supervised model is shown to outperform existing baselines across various clinically relevant computational pathology tasks.
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“By increasing the patch size from the widely used 256 × 256 pixels, we effectively reduce the sequence length by four without impacting the representation quality due to higher resolution patch input, leveraging the robustness of the patch-level foundation models in generalizing to higher resolutions9,10,87.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To overcome these limitations, several research groups have alternatively proposed the use of weakly-supervised deep learning models, which can be trained to perform various downstream computational pathology tasks such as tumor subtyping and prognosis32–37 without any region or pixel-level annotations.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“UNI [191], Prov-GigaPath [192], and Virchow [193] were pretrained solely on pathology image data to perform cancer classification, diagnosis, and prognosis.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [191]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The vision module is based on the patch encoder CTransPath17 for the small and medium models, and UNI18 for the large model.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“We found out that the UNI foundation model32, when paired with multi-task AB-MIL, achieved the best predictive performance for the various TME signature prediction tasks.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [32]
In the citing paper
“In fact GMA is widely used to assess the performance of pathology foundation models23,25,26.”
From A clinical benchmark of public self-supervised pathology foundation models (2025) · cites this paper as [23]
In the citing paper
“Self-supervised learning has become a widely used approach for pretraining foundation models on thousands to tens of thousands of images without annotations or labels, marking an important paradigm shift in the field.20,21”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [20]
In the citing paper
“An increasing number of published studies show the potential of foundation models20,24; however, they are currently underrepresented in the field of liver diseases.4”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [20]
In the citing paper
“we present Virchow, the largest foundation model for computational pathology to date.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“Despite the challenges in collecting large-scale datasets in the pathology domain, recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters35–42”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“Built on rich knowledge representations, these models have shown impressive performance across medical fields such as ophthalmology20, radiology21 and pathology22–25.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
In the citing paper
“Specialty-specific foundation models20,21,23 typically focus on single imaging modalities”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
In the citing paper
“The scaling behavior observed in PanDerm’s performance aligns with recent foundation model trends20,22,23,59”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
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Self-citationA deep learning model to predict RNA-Seq expression of tumours from whole-slide images
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
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Rotation Equivariant CNNs for Digital Pathology
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CellViT: Vision Transformers for precise cell segmentation and classification
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Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
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Florence: A New Foundation Model for Computer Vision
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Self-citationA proteogenomic portrait of lung squamous cell carcinoma
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Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
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Scaling and Benchmarking Self-Supervised Visual Representation Learning
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Artificial intelligence for digital and computational pathology
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Self-citationThe impact of site-specific digital histology signatures on deep learning model accuracy and bias
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WSISA: Making Survival Prediction from Whole Slide Histopathological Images
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
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Self supervised contrastive learning for digital histopathology
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Self-citationWeakly supervised histopathology cancer image segmentation and classification
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Self-citationMultistain deep learning for prediction of prognosis and therapy response in colorectal cancer
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Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
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Neural Image Compression for Gigapixel Histopathology Image Analysis
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SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
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BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
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Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
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RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval
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Graph CNN for Survival Analysis on Whole Slide Pathological Images
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Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations
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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
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Self-citationA Cookbook of Self-Supervised Learning
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Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
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OncoTree: A Cancer Classification System for Precision Oncology
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Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
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Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
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A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
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SEED: Self-supervised Distillation For Visual RepresentationPreprint
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Self-citationVisual pattern mining in histology image collections using bag of features
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PAIP 2019: Liver cancer segmentation challenge
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Self-citationVisual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
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Self-citationSelf-supervised driven consistency training for annotation efficient histopathology image analysis
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Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies
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Data Contamination: From Memorization to Exploitation
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Machine learning in computational histopathology: Challenges and opportunities
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Deep Multi-instance Learning for Survival Prediction from Whole Slide Images
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Pan-cancer image-based detection of clinically actionable genetic alterationsPreprint
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The Digital Brain Tumour Atlas, an open histopathology resource
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Universal encoding of pan-cancer histology by deep texture representations
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Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma
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Virchow: A Million-Slide Digital Pathology Foundation ModelPreprint
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Handcrafted Histological Transformer (H2T): Unsupervised representation of whole slide images
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Quilt-1M: One Million Image-Text Pairs for Histopathology
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Self-citationWeakly supervised joint whole-slide segmentation and classification in prostate cancer
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Rapid generation of long, chemically modified pegRNAs for prime editing
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Self-citationGiga-SSL: Self-Supervised Learning for Gigapixel Images
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Gigapixel Whole-Slide Images Classification Using Locally Supervised Learning
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Hierarchical Discriminative Learning Improves Visual Representations of Biomedical Microscopy
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EVA: Exploring the Limits of Masked Visual Representation Learning at ScalePreprint
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CellViT: Vision Transformers for Precise Cell Segmentation and ClassificationPreprint
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Learning Visual Representations via Language-Guided Sampling
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Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide ImagesPreprint
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Towards a Visual-Language Foundation Model for Computational PathologyPreprint
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Self-citationSGCL: Spatial guided contrastive learning on whole-slide pathological images
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Brummer, O., Pölönen, P., Mustjoki, S. M., & Brück, O. E. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02329-4 · Cited by 13
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Jaume, G., Vaidya, A. J., Chen, R., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.06819 · Cited by 10
Self-citationVisual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology ImagesPreprint
Lu, M. Y., Chen, B., Zhang, J. A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.07831 · Cited by 10
Self-citationHörst, F., Ting, S. C., Liffers, S., Pomykala, K. L., Steiger, K., Albertsmeier, M., Angele, M. K., Lorenzen, S., Quante, M., Weichert, W., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00038 · Cited by 9
SLPD: Slide-Level Prototypical Distillation for WSIs
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Jiang, C., Hou, X., Kondepudi, A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.01605 · Cited by 6
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Domain-specific optimization and diverse evaluation of self-supervised models for histopathologyPreprint
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Self-citationMedical AI Research Foundations: A repository of medical foundation models
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Flagged first, then by citations
Research works printed but not linked: 13
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Cited by
Sorted by citations
A whole-slide foundation model for digital pathology from real-world data
Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921
“Existing models often treat each image tile as an independent sample and formulate slide-level modelling as multiple instance learning 4,40–43 , thus limiting their ability to model complex global patterns in gigapixel whole slides.”
A foundation model for clinical-grade computational pathology and rare cancers detection
Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03141-0 · Cited by 572
“Implementations of other pretrained visual encoders benchmarked in the study were obtained from the following links: UNI (https://huggingface.co/MahmoodLab/UNI), Phikon (https://huggingface.co/owkin/phikon), DINOp=8 (https://github. com/lunit-io/benchmark-ssl-pathology), PLIP (https://huggingface.co/ vinid/plip), CTra…”
InfluentialA pathology foundation model for cancer diagnosis and prognosis prediction
Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580
A multimodal generative AI copilot for human pathology
Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Zhao, M., Chow, A. K., Ikemura, K., Kim, A., Pouli, D., Patel, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07618-3 · Cited by 461
Self-citationHow to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393
“145 Lastly, in live-cell imaging, CNNs are employed to track dynamic changes within cells or even single molecules over time, providing insights into cell migration, responses to treatment, or the movement and interaction of individual molecules within cells, revealing crucial biological processes…”
Metadata
- Authors
- Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., Williams, M., Oldenburg, L., Weishaupt, L. L., Wang, J. J., Vaidya, A., Le, L. P., Gerber, G., Sahai, S., Williams, W., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-03
- Volume
- 30
- Issue
- 3
- Pages
- 850-862
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11403354
- OpenAlex
- W4392947521
- Semantic Scholar
- cac496ddebbff6bbba78242780cde01bd962bfc9
- Cited by
- 1,560
- References
- 170
- Citations by year
- 2026: 789 · 2025: 613 · 2024: 154
- FWCI
- 229.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (T32HG002295)
- National Cancer Institute (T32CA251062)
- National Institute of General Medical Sciences (R35GM149270)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- computational pathology, whole slide images, self-supervised learning, foundation models, unsupervised feature learning, tissue classification, few-shot learning, cancer subtyping, histopathology, transfer learning
- MeSH terms
- Artificial Intelligence, Workflow
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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