A pathology foundation model for cancer diagnosis and prognosis prediction
Open accessAbstract
Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.
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Current slide foundation models still require end-to-end training or fine-tuning and lack the capability of learning transferable slide representations for challenging clinical scenarios.
“Even with multimodal techniques such as vision-language pretraining that augment the pretraining dataset with pathology reports, current slide foundation models still require end-to-end training or fine-tuning and lack the capability of learning transferable slide representations for challenging clinical scenarios58,82,83.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [83]
The Introduction says earlier image–text methods focused mainly on individual tiles and had not evaluated whether their features generalized across tasks, cancer types, and data sources. Online Methods reports fine-tuning CHIEF for its prediction tasks. But the Abstract and Discussion report that CHIEF learned generalizable whole-slide features across clinical applications, so the claim that current slide foundation models lack that capability overstates what this paper says. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSlide foundation models are developed with orders of magnitude fewer samples and limited optimization of SSL recipes compared to patch foundation models, leading to slide representations with restricted generalization capability.
“Second, whereas current patch foundation models are trained with millions of histology image patches, slide foundation models are developed with orders of magnitude fewer samples and limited optimization of SSL recipes, leading to slide representations with restricted generalization capability58,62,82,83.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [83]
The Introduction reports CHIEF pretraining on 15 million image tiles and 60,530 whole-slide images, supporting the difference in sample counts. The paper does not say slide models received limited SSL-recipe optimization; its Abstract and Discussion instead describe CHIEF's slide representations as generalizable across independent datasets. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI agents continuously evolve through iterative learning from new datasets, enabling them to adapt to emerging medical knowledge and evolving clinical standards.
“Moreover, AI agents continuously evolve through iterative learning from new datasets, enabling them to adapt to emerging medical knowledge and evolving clinical standards.73,74”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [74]
The Online Methods sections on CHIEF pretraining and fine-tuning describe training on multiple datasets and adapting the pretrained model to specific pathology tasks (page not specified). They do not describe AI agents continuously learning from newly arriving datasets or adapting to emerging medical knowledge and changing clinical standards. (LLM, conf=0.78)
Experimental — accuracy not yet measuredAI-driven medical imaging analysis significantly improves the accuracy and efficiency of diagnostic processes.
“AI-driven medical imaging analysis has emerged as a pivotal tool in modern healthcare, significantly improving the accuracy and efficiency of diagnostic processes.66,74,76,77,78,79,80,81,82,83”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [74]
The Results section (‘CHIEF augmented cancer cell detection’; page unavailable) reports significantly better cancer-detection performance than comparator AI methods, supporting improved accuracy for this pathology-imaging task. The Introduction describes efficient whole-slide image feature aggregation, but the paper does not establish that AI imaging improves the efficiency of clinical diagnostic processes. Its findings also concern cancer histopathology rather than medical imaging generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCHIEF considered tissue origin as a label by applying a weakly supervised method during pretraining.
“CHIEF considered the tissue origin as a label by applying a weakly supervised method during pretraining [196].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [196]
The Online Methods (CHIEF Model Architecture; page unavailable) describe weakly supervised pretraining and say anatomical-site information was added as text to enhance supervision. The site was supplied in a text prompt, however, rather than described as a tissue-origin label for the weakly supervised task. The Online Methods (Evaluation; page unavailable) describe a separate fine-tuning step for tumor-origin prediction. (LLM, conf=0.83)
Experimental — accuracy not yet measured
CHIEF applies attention-based feature aggregation, trained via slide-level contrastive learning and anatomic site information.
“CHIEF83 applies attention-based feature aggregation, trained via slide-level contrastive learning and anatomic site information.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [83]
From cancer subtyping to molecular classification, TITAN consistently outperforms other state-of-the-art slide encoders such as PRISM, GigaPath and CHIEF.
“From cancer subtyping to molecular classification, TITAN consistently outperforms other state-of-the-art slide encoders, such as PRISM62, GigaPath58 and CHIEF83.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [83]
Foundation models have shown impressive performance across medical fields such as ophthalmology, radiology and pathology.
“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 [25]
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Self-citationA multimodal whole-slide foundation model for pathology
Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03982-3 · Cited by 171 · see citing works
“…current patch foundation models are trained with millions of histology image patches, slide foundation models are developed with orders of magnitude fewer samples and limited optimization of SSL recipes, leading to slide representations with restricted generalization capability 58,62,82,83 .”
InfluentialA Multimodal Vision Foundation Model for Clinical Dermatology
Yan, S., Yu, Z., Primiero, C., Vico-Alonso, C., Wang, Z., Yang, L., Tschandl, P., Hu, M., Ju, L., Tan, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03747-y · Cited by 117 · see citing works
“6 and Supplementary Tables 22–25.”
InfluentialMachine learning methods for histopathological image analysis: updates in 2024
Komura, D., Ochi, M., & Ishikawa, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.033 · Cited by 76 · see citing works
Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Biomedicines · doi:10.3390/biomedicines13040951 · Cited by 74 · see citing works
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
Self-citationBenchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80 · see citing works
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Jin, C., Yang, S., Li, J., Zhang, Z., Zhao, C., Zhou, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.15362 · Cited by 60 · see citing works
Self-citationGenerative Artificial Intelligence in Anatomic Pathology
Brodsky, V., Ullah, E., Bychkov, A., Song, A. H., Walk, E. E., Louis, P., Rasool, G., Singh, R. S., Mahmood, F., Bui, M. M., et al. (2025) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2024-0215-ra · Cited by 48 · see citing works
A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41 · see citing works
“73,74 This dynamic learning process ensures that AI agents remain aligned with the latest advancements in medical science, further enhancing their diagnostic capabilities.”
Self-citationShao, A., Liu, X., Shen, W., Li, Y., Wu, H., Pan, X., Yang, Z., Xu, Y., Zhu, T., Wang, Y., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01759-z · Cited by 16 · see citing works
Self-citationHuang, Z., Kaur Sekhon, V., Sadeghian, R., Vaida, M. L., Jo, C., McCormick, B. A., Ward, D. V., Bucci, V., & Haran, J. P. (2025) · IEEE Access · doi:10.1109/access.2025.3599857 · Cited by 10 · see citing works
Image Analysis: Understanding and Mitigating Batch Effects in Histopathology
Gindra, R. H., Boushehri, S. S., Wagner, S. J., Tran, M., Winter, D. J. E., Schnabel, J. A., Saur, D., Marr, C., & Peng, T. (2025) · Trillium Pathology · doi:10.47184/tp.2025.01.03 · Cited by 2 · see citing works
PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
Vorontsov, E., Shaikovski, G., Casson, A., Viret, J., Zimmermann, E., Tenenholtz, N., Wang, Y. K., Bernhard, J. H., Godrich, R. A., Retamero, J. A., et al. (2025) · arXiv · doi:10.48550/arxiv.2506.13063
- Authors
- Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., Wang, F., Peng, Y., Zhu, J., Zhang, J., Jackson, C. R., Zhang, J., Dillon, D., Lin, N. U., Sholl, L., Denize, T., Meredith, D., Ligon, K. L., Signoretti, S., Ogino, S., Golden, J. A., Nasrallah, M. P., Han, X., Yang, S., & Yu, K.-H.
- Affiliations
- Harvard University (US)
- Sichuan University (CN)
- Sun Yat-sen University Cancer Center (CN)
- Tencent (China) (CN)
- Stanford Medicine (US)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Chongqing University (CN)
- Qingdao University (CN)
- First Affiliated Hospital of Jinan University (CN)
- Sun Yat-sen University (CN)
- Pennsylvania State University (US)
- Brigham and Women's Hospital (US)
- Broad Institute (US)
- Cedars-Sinai Medical Center (US)
- University of Pennsylvania (US)
- Corresponding author
- Yang, Sen
- Yu, Kun-Hsing
- ORCID
- Wang, Xiyue
- Zhao, Junhan
- Marostica, Eliana
- Yuan, Wei
- Zhang, Jiayu
- Li, Ruijiang
- Tang, Hongping
- Wang, Kanran
- Li, Yu
- Peng, Yulong
- Zhang, Jing
- Zhang, Jun
- Dillon, Deborah
- Lin, Nancy U.
- Sholl, Lynette
- Denize, Thomas
- Meredith, David
- Ligon, Keith L.
- Signoretti, Sabina
- Ogino, Shuji
- Golden, Jeffrey A.
- Nasrallah, MacLean P.
- Han, Xiao
- Yang, Sen
- Yu, Kun-Hsing
- Venue
- Nature
- Published
- 2024-09-04
- Volume
- 634
- Issue
- 8035
- Pages
- 970-978
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC12186853
- OpenAlex
- W4402220065
- Semantic Scholar
- 6c7ebe65fd98ed353b3dc35922cdef09850716dd
- Cited by
- 580
- References
- 64
- Citations by year
- 2026: 304 · 2025: 259 · 2024: 17
- FWCI
- 85.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- ScholarSphere (Penn State Libraries) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Defense
- Brigham and Women's Hospital
- Nvidia
- Seoul National University
- Seoul National University Hospital
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL174679)
- National Cancer Institute (P50CA165962)
- National Institute of General Medical Sciences (R35GM142879)
- DOD Peer Reviewed Cancer Research Program
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- histopathology, whole slide images, foundation models, cancer diagnosis, outcome prediction, unsupervised pre-training, domain shift, digital pathology, cancer cell detection, cancer subtyping
- MeSH terms
- Supervised Machine Learning, Deep Learning, Female, Histocytochemistry, Humans, Male, Neoplasms, Pathology, Clinical, Pattern Recognition, Automated, Prognosis, Sensitivity and Specificity, Reproducibility of Results, Datasets as Topic
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