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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Increasing the patch size from 256 × 256 pixels to 512 × 512 pixels reduces the sequence length by four without impacting representation quality, leveraging the robustness of patch-level foundation models to higher resolutions.
“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]
The Results section, ‘Robustness to high image resolution’ (page unavailable), reports that UNI retains relatively strong feature quality across image resolutions, but its KNN performance falls by 6.3% at higher resolution in one task. The Methods describe extracting 256 × 256 tissue patches, but do not test changing them to 512 × 512 or report a fourfold reduction in slide-level sequence length. Thus the source supports the general robustness premise, not the claim of unchanged quality for that specific patch-size change. (LLM, conf=0.88)
Experimental — accuracy not yet measuredWeakly-supervised deep learning models can be trained to perform various downstream computational pathology tasks such as tumor subtyping and prognosis without any region or pixel-level annotations.
“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]
The Results section on weakly supervised slide classification reports tumor-subtyping tasks, and the Results section on prompt-based slide classification says that weakly supervised learning via multiple-instance learning no longer requires region-of-interest annotations. The cited paper does not report prognosis as one of its weakly supervised downstream tasks; prognosis appears in reference titles rather than in the paper’s account of those tasks. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredUNI, Prov-GigaPath, and Virchow were pretrained solely on pathology image data to perform cancer classification, diagnosis, and prognosis.
“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]
The Introduction describes UNI as pretrained on H&E histology images, and the Results report its use for cancer classification and diagnostic tasks. The cited paper does not report a UNI prognosis task, so that part of the claim is unsupported. Citation [191] identifies UNI; this verdict does not assess the separately cited Prov-GigaPath or Virchow. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe vision module is based on the patch encoder CTransPath for the small and medium models, and UNI for the large model.
“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 UNI foundation model, when paired with multi-task AB-MIL, achieved the best predictive performance for the various TME signature prediction tasks.
“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]
GMA is widely used to assess the performance of pathology foundation models.
“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]
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.
“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]
An increasing number of published studies show the potential of foundation models, but foundation models are currently underrepresented in the field of liver diseases.
“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]
Virchow is the largest foundation model for computational pathology to date.
“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]
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.
“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]
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 parameters.
“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]
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 [23]
Specialty-specific foundation models typically focus on single imaging modalities.
“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]
PanDerm's scaling behavior aligns with recent foundation model trends.
“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-citationGiga-SSL: Self-Supervised Learning for Gigapixel Images
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EVA: Exploring the Limits of Masked Visual Representation Learning at ScalePreprint
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Hierarchical Discriminative Learning Improves Visual Representations of Biomedical Microscopy
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CellViT: Vision Transformers for Precise Cell Segmentation and ClassificationPreprint
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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-citationLearning Visual Representations via Language-Guided Sampling
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A Foundational Multimodal Vision Language AI Assistant for Human PathologyPreprint
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SGCL: Spatial guided contrastive learning on whole-slide pathological images
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Self-citationVisual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology ImagesPreprint
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SLPD: Slide-Level Prototypical Distillation for WSIs
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Domain-specific optimization and diverse evaluation of self-supervised models for histopathologyPreprint
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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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“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…”
A Vision-Language Foundation Model for Precision Oncology
Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., et al. (2025) · Nature · doi:10.1038/s41586-024-08378-w · Cited by 256 · see citing works
“2a and Supplementary Tables 1 and 2).”
Vision-language models for medical report generation and visual question answering: a review
Hartsock, I., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1430984 · Cited by 257 · see citing works
A guide to artificial intelligence for cancer researchers
Pérez-López, R., Laleh, N. G., Mahmood, F., & Kather, J. N. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00694-7 · Cited by 220 · see citing works
“Cross-modality applications refer to AI systems that can process and integrate information from different data types, such as combining radiology images with clinical notes or pathology images with genomic data 98 , or generally, images and text 97 .”
InfluentialSelf-citationA review of deep learning for brain tumor analysis in MRI
Dorfner, F. J., Patel, J., Kalpathy–Cramer, J., Gerstner, E. R., & Bridge, C. P. (2025) · npj Precision Oncology · doi:10.1038/s41698-024-00789-2 · Cited by 249 · see citing works
“Chen et al. recently intro-ducedageneral-purposefoundationmodelforpathologythatwaspretrained onmorethan100millionimagesacquiredfromover100,000diagnostic H&E-stainedWSIsacross20differenttissuetypes 109 .”
A 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
“…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 resolutions 9,10,87 .”
InfluentialSelf-citationLiu, Y., Dai, Y., & Wang, L. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.12.009 · Cited by 146 · see citing works
“Building on methods that align histological features to refine spatial units, image data from many spatial platforms can be fed into foundation models, such as UNI 80 for H&E images and KRONOS 81 for fluorescence images, to extract representative features.”
A clinical benchmark of public self-supervised pathology foundation models
Campanella, G., Chen, S., Singh, M., Verma, R., Muehlstedt, S., Zeng, J., Stock, A., Croken, M., Veremis, B., Elmas, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58796-1 · Cited by 126 · see citing works
“Chen et al. 23 introduced UNI, a ViT-large model trained on 100,000 proprietary slides using the DINOv2 24 SSL algorithm.”
Demographic bias in misdiagnosis by computational pathology models
Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Song, A. H., Jaume, G., Yang, Y., Hartvigsen, T., Dyer, E., Lu, M. Y., Lipková, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02885-z · Cited by 124 · see citing works
Self-citationFoundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions
He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3496744 · Cited by 130 · see citing works
“UNI [121] utilizedtheDINOv2[122]paradigmandintegratedtheMIMand CLtolearnthefoundationmodelforcomputationalpathology.”
Self-citationA visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
Foundation models for fast, label-free detection of glioma infiltration
Kondepudi, A., Pekmezci, M., Hou, X., Scotford, K., Jiang, C., Rao, A. T., Harake, E. S., Chowdury, A., Al‐Holou, W. N., Wang, L., et al. (2024) · Nature · doi:10.1038/s41586-024-08169-3 · Cited by 119 · see citing works
“Foundation models for medical AI have the potential to solve challenging Article clinical tasks by requiring little to no task-specific annotations to achieve good generalization performance and clinically meaningful results 17–19 .”
Analysis of 3D pathology samples using weakly supervised AI
Song, A. H., Williams, M., Williamson, D. F. K., Chow, S. S. L., Jaume, G., Gao, G., Zhang, A., Chen, B., Baras, A. S., Serafin, R. J., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.035 · Cited by 108 · see citing works
Self-citationThe generative era of medical AI
Fahrner, J., Chen, E., Topol, E. J., & Rajpurkar, P. (2025) · Cell · doi:10.1016/j.cell.2025.05.018 · Cited by 118 · see citing works
Swin-UMamba†: Adapting Mamba-Based Vision Foundation Models for Medical Image Segmentation
Liu, J., Yang, H., Zhou, H.-Y., Yu, L., Liang, Y. Q., Yu, Y., Zhang, S., Zheng, H., & Wang, S. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3508698 · Cited by 104 · see citing works
“Recent research has demonstrated vision foundation models [13], [14], [15], [16], [17], [18], [19], [20] can achieve remarkable performance without relying on large-scale high-quality labeled datasets for various medical tasks.”
A 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
“Specialty-specific foundation models 20,21,23 typically focus on single imaging modalities, while general biomedical models, despite their broad scope, struggle with domain-specific data scarcity and integrating heterogeneous modalities for comprehensive clinical analysis.”
Marra, A., Morganti, S., Pareja, F. G., Campanella, G. S. V., Bibeau, F., Fuchs, T. J., Loda, M. F., Parwani, A. V., Scarpa, A., Reis-Filho, J. S., et al. (2025) · Annals of Oncology · doi:10.1016/j.annonc.2025.03.006 · Cited by 108 · see citing works
Yang, Z., Wei, T., Liang, Y., Yuan, X., Gao, R., Xia, Y., Zhou, J., Zhang, Y., & Yu, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-57587-y · Cited by 97 · see citing works
“Subsequently, the foundation models are well fi ne-tuned using small labeled datasets customized for speci fi c tasks and always achieve high performance 16,17 .”
Machine 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 in digital pathology — time for a reality check
Aggarwal, A., Bharadwaj, S., Corredor, G., Pathak, T. B., Badve, S., & Madabhushi, A. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-00991-6 · Cited by 78 · see citing works
“The focus has progressively shifted from basic tasks, such as image classification, segmentation and detection, to more complex applications, including predicting patient prognosis and response to treatment 11,15,21,22,36 .”
Avanzo, M., Stancanello, J., Pirrone, G., Drigo, A., & Retico, A. (2024) · Cancers · doi:10.3390/cancers16213702 · Cited by 80 · see citing works
“ML can also assist in diagnosing conditions from clinical data, such as myocardial infarction [190,191] and in making differential diagnoses among various findings in neonatal radiographs [192].”
BackgroundA multimodal visual–language foundation model for computational ophthalmology
Shi, D., Zhang, W., Yang, J., Huang, S., Chen, X., Xu, P., Jin, K., Lin, S. C., Wei, J., Yusufu, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01772-2 · Cited by 94 · see citing works
Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
Lin, Q., Zhu, Y., Mei, X., Huang, L., Ma, J., He, K., Peng, Z., Cambria, E., & Feng, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102795 · Cited by 76 · see citing works
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Song, A. H., Chen, R. J., Ding, T., Williamson, D. F. K., Jaume, G., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01099 · Cited by 79 · see citing works
“Given a WSI for subject j , we tessellate it into small non-overlapping patches We then employ a feature extractor f enc ( · ) pre-trained on large archives of histopathology images [18], to extract a representative and compressed embedding from each patch.”
BackgroundMethodSelf-citationDigital profiling of gene expression from histology images with linearized attention
Pizurica, M., Zheng, Y., Carrillo‐Pérez, F., Noor, H., Yao, W., Wohlfart, C., Vladimirova, A., Kathleen Marchal, D., & Gevaert, O. (2024) · Nature Communications · doi:10.1038/s41467-024-54182-5 · Cited by 81 · see citing works
“In addition, we leverage UNI, a foundation model optimized for histological feature extraction 24 .”
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
“UNI [191], Prov-GigaPath [192], and Virchow [193] were pretrained solely on pathology image data to perform cancer classification, diagnosis, and prognosis.”
Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection
Campanella, G., Kumar, N., Nanda, S., Singi, S., Fluder, E., Kwan, R., Muehlstedt, S., Pfarr, N., Schüffler, P. J., Häggström, I., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03780-x · Cited by 69 · see citing works
Benchmarking 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
“In the first step, feature vectors were extracted from WSIs utilizing the foundational models evaluated in this study.”
InfluentialGrandQC: A comprehensive solution to quality control problem in digital pathology
Weng, Z., Seper, A., Pryalukhin, A., Mairinger, F. D., Wickenhauser, C., Bauer, M., Glamann, L., Bläker, H., Lingscheidt, T., Hulla, W., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54769-y · Cited by 67 · see citing works
Zhao, Y. S., Xiong, S., Ren, Q., Wang, J., Li, M., Yang, L., Wu, D., Tang, K., Pan, X., Chen, F., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00599-0 · Cited by 67 · see citing works
Self-citationPantanowitz, L., Pearce, T. M., Abukhiran, I. M., Hanna, M. G., Wheeler, S., Soong, T. R., Tafti, A. P., Pantanowitz, J., Lu, M. Y., Mahmood, F., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100680 · Cited by 70 · see citing works
Self-citationHu, B., He, R., Pang, K., Wang, G., Wang, N., Zhu, W., Sui, X., Teng, H., Liu, T., Zhu, J., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.023 · Cited by 65 · see citing works
SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3586694 · Cited by 67 · see citing works
“…medical VFMs and the adaptation of existing natural image VFMs. Modality-specific models like RETFound [25], MIS-FM [26], SegVol [27], STU-Net [5], USFM [23], and UNI [28] have shown high generalization within their respective domains, partially mitigating the challenge of limited annotated data.”
Artificial intelligence-based biomarkers for treatment decisions in oncology
Ligero, M., Nahhas, O. S. M. E., Aldea, M. D., & Kather, J. N. (2025) · Trends in cancer · doi:10.1016/j.trecan.2024.12.001 · Cited by 70 · see citing works
Domain Generalization in Computational Pathology: Survey and Guidelines
Jahanifar, M., Raza, M., Xu, K., Vuong, T. T. L., Jewsbury, R., Shephard, A., Tajeddin, N. Z., Kwak, J. T., Raza, S. E. A., Minhas, F., et al. (2025) · ACM Computing Surveys · doi:10.1145/3724391 · Cited by 67 · see citing works
“In non-subjective tasks like survival analysis prediction, a posterior shift may indicate a prior shift or sample preparation disparities.”
Combining spatial transcriptomics with tissue morphology
Chelebian, E., Avenel, C., & Wählby, C. (2025) · Nature Communications · doi:10.1038/s41467-025-58989-8 · Cited by 66 · see citing works
“Rapid advancements in 2024 have led to the development of models like CONCH 86 , CTrans-Path 87 , H-optimus-0 88 , Kaiko 89 , Phikon 90 , Prov-GigaPath 91 , UNI 92 , or Virchow 93 , showcasing state-of-the-art representation learning.”
A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies
Huang, Z., Yang, E., Shen, J., Gratzinger, D. A., Eyerer, F., Liang, B., Nirschl, J., Bingham, D. B., Dussaq, A., Kunder, C. A., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01223-5 · Cited by 62 · see citing works
“…(AI) and machine learning (ML) algorithms have made substantial achievements in digital pathology, enabling a variety of computer-aided diagnoses; these include cell or tissue segmentation 1,2 , image classification 3–6 , patient prognosis 7,8 , and lymph node (LN) metastasis identification 9,10 .”
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. (2025) · Nature Communications · doi:10.1038/s41467-025-66220-x · Cited by 74 · see citing works
“In the realm of prior investigations into pathology foundation models, two prominent categories have emerged: vision-only models 2,4,46 and vision-language models 1,3 .”
InfluentialSelf-citationA 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
“In the realm of prior investigations into pathology foundation models, two prominent categories have emerged: vision-only models 2,4,46 and vision-language models 1,3 .”
InfluentialSelf-citationMultimodal AI generates virtual population for tumor microenvironment modeling
Valanarasu, J. M. J., Xu, H., Usuyama, N., Kim, C., Wong, C., Argaw, P., Shimol, R. B., Crabtree, A., Matlock, K., Bartlett, A. Q., et al. (2025) · Cell · doi:10.1016/j.cell.2025.11.016 · Cited by 58 · see citing works
An eyecare foundation model for clinical assistance: a randomized controlled trial
Wu, Y.-L., Qian, B., Li, T., Qin, Y., Guan, Z., Chen, T., Jia, Y., Zhang, P., Zeng, D., Moroi, S. E., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03900-7 · Cited by 60 · see citing works
Self-citationLi, Z., Li, Y., Xiang, J., Wang, X., Yang, S., Zhang, X., Eweje, F. R., Chen, Y. M., Luo, X., Li, Y., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04060-4 · Cited by 61 · see citing works
“25).”
New horizons at the interface of artificial intelligence and translational cancer research
Yates, J., & Allen, E. M. V. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.018 · Cited by 55 · see citing works
Arango-Argoty, G., Bikiel, D. E., Sun, G. J., Kipkogei, E., Smith, K. M., Pro, S. C., Choe, E. Y., & Jacob, E. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.029 · Cited by 54 · see citing works
Transcriptomics-Guided Slide Representation Learning in Computational Pathology
Jaume, G., Oldenburg, L., Vaidya, A., Chen, R. J., Williamson, D. F. K., Peeters, T., Song, A. H., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00920 · Cited by 51 · see citing works
“Encoding histology patches: Most works applying SSL to CPath focus on building embeddings from image patches (typically 256 × 256-pixel regions) [4, 12, 13, 21, 40, 43, 55, 81, 83, 85].”
MethodSelf-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
Self-citationROSIE: AI generation of multiplex immunofluorescence staining from histopathology images
Wu, E., Bieniosek, M. F., Wu, Z., Thakkar, N., Charville, G. W., Makky, A., Schürch, C. M., Huyghe, J. R., Peters, U., Li, C. I., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62346-0 · Cited by 47 · see citing works
“Recent advances in Transformer architecture-based foundation models trained on histopathology images 10,12,39 show promise when adapted and fi ne-tuned on a wide range of clinical downstream tasks.”
Breen, J., Allen, K., Zucker, K., Godson, L., Orsi, N. M., & Ravikumar, N. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00799-8 · Cited by 52 · see citing works
Bian, Y., Li, J., Ye, C., Jia, X., & Yang, Q. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003489 · Cited by 48 · see citing works
“[81,82] Based on pretrained dataset modalities and clinical scenarios, foundation models are typically classified into three categories: general vision foundation models, modality-specific vision foundation models, and vision-language foundation models [Table 2].”
An Electrocardiogram Foundation Model Built on over 10 Million Recordings
Li, J., Aguirre, A. D., Junior, V. F. M., Jin, J., Liu, C., Zhong, L., Sun, C., Clifford, G. D., Westover, M. B., & Hong, S. (2025) · NEJM AI · doi:10.1056/aioa2401033 · Cited by 62 · see citing works
Redlich, J.-P., Feuerhake, F., Weis, J., Schaadt, N. S., Teuber‐Hanselmann, S., Buck, C., Luttmann, S., Eberle, A., Nikolin, S., Appenzeller, A., et al. (2024) · npj Imaging · doi:10.1038/s44303-024-00020-8 · Cited by 47 · see citing works
“The emergence of foundationmodelsforcomputationalpathology 123,124 ,developedusinglarge amountsofhistopathologicaldata,maybeanimprovementoverthecurrent relianceontheResNetCNNarchitectureandtheImageNetdatasetforpre-training.”
Mahlein, A., Barbedo, J. G. A., Chiang, K., Ponte, E. M. D., & Bock, C. H. (2024) · Phytopathology · doi:10.1094/phyto-01-24-0009-per · Cited by 53 · see citing works
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Koch, V., Wagner, S. J., Kazeminia, S., Sancar, E., Hehr, M., Schnabel, J. A., Peng, T., & Marr, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_49 · Cited by 42 · see citing works
Zhang, J., Nguyen, A. T., Xi, H., Trinh, V. Q., Qin, H., Samaras, D., & Hosseini, M. S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00339 · Cited by 42 · see citing works
Ma, J., Guo, Z., Zhou, F., Wang, Y., Xu, Y., Li, J., Yan, F., Cai, Y., Zhu, Z., Jin, C., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01488-4 · Cited by 59 · see citing works
Self-citationKhosravi, P., Fuchs, T. J., & Ho, D. J. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-3630 · Cited by 41 · see citing works
Mi, H., Sivagnanam, S., Ho, W. J., Zhang, S., Bergman, D. R., Deshpande, A. M., Baras, A. S., Jaffee, E. M., Coussens, L. M., Fertig, E. J., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae421 · Cited by 38 · see citing works
“…of robust, general-purpose foundation models has been successful in a wide range of downstream tasks involving histopathology images and/or text,with state-of-the-art performance on image classification [246–248], segmentation [71, 237], and text-to-image and image-to-text retrieval [249, 250].”
BackgroundWang, X., Jiang, Y., Yang, S., Wang, F., Zhang, X., Wang, W., Chen, Y. M., Wu, X., Xiang, J., Li, Y., et al. (2025) · Journal of Clinical Oncology · doi:10.1200/jco-24-01501 · Cited by 43 · see citing works
Patkar, S., Chen, A., Basnet, A., Bixby, A., Rajendran, R., Chernet, R., Faso, S., Kumar, P. A., Desai, D., El-Zammar, O., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00765-w · Cited by 41 · see citing works
“Overall, to develop HistoTME we experiment with three open-source foundation models — CTransPath, RetCCL, and UNI 21,22,26 — and two con fi gurations of AB-MIL: single task AB-MIL, where the predictions of each signature are optimized separately, and multi-task AB-MIL, where predictions of functionally related signatur…”
InfluentialAdvancing biological understanding of cellular senescence with computational multiomics
Li, S., Agudelo Garcia, P. A., Aliferis, C., Becich, M. J., Calyeca, J., Cosgrove, B. D., Elisseeff, J., Farzad, N., Fertig, E. J., Glass, C., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02314-y · Cited by 40 · see citing works
Self-citationA review on federated learning in computational pathology
Schoenpflug, L. A., Nie, Y., Sheikhzadeh, F., & Koelzer, V. H. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.10.037 · Cited by 40 · see citing works
Generating dermatopathology reports from gigapixel whole slide images with HistoGPT
Tran, M., Schmidle, P., Guo, R. R., Wagner, S. J., Koch, V., Lupperger, V., Novotny, B., Murphree, D. H., Hardway, H. D., D’Amato, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60014-x · Cited by 36 · see citing works
“The vision module is based on thepatchencoder CTransPath 17 for thesmall and medium models,and UNI 18 for the large model.”
InfluentialGe, Y., Leng, J., Tang, Z., Wang, K., Kaicheng, U., Zhang, S. M., Han, S., Zhang, Y., Xiang, J., Yang, S., et al. (2024) · Research · doi:10.34133/research.0568 · Cited by 34 · see citing works
“Afterward, UNI [ 37 ], directly training on 100 million tiles using DINOv2 [ 40 ] architecture, was successfully validated by 33 pathology analytical tasks.”
Comiter, C. S., Chen, X., Vaishnav, E. D., Kobayashi-Kirschvink, K. J., Ciampricotti, M., Zhang, K., Murray, J., Monticolo, F., Qi, J., Tanaka, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.21.533680 · Cited by 33 · see citing works
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
Zhou, H., Zhou, F., & Chen, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3455931 · Cited by 33 · see citing works
“To prove the robustness of our framework, we compare the proposed framework with main competitors, including MCAT [29], SurvPath [30], MOTCat [32], and CMTA [33] by using latest pathology foundation models, including HIPT [16], PLIP [52], UNI [54], and CONCH [53].”
Howard, F. M., Hieromnimon, H. M., Ramesh, S., Dolezal, J. M., Kochanny, S. E., Zhang, Q., Feiger, B., Peterson, J., Fan, C., Perou, C. M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adq0856 · Cited by 33 · see citing works
Yuan, B., Hu, Y., Liang, Y. C., Zhu, Y., Zhang, L., Cai, S., Peng, R., Wang, X., Yang, Z., & Hu, J. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1606336 · Cited by 37 · see citing works
Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024
Žigutytė, L., Sorz-Nechay, T., Clusmann, J., & Kather, J. N. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101209 · Cited by 33 · see citing works
Cai, L., Huang, S., Zhang, Y., Lu, J., & Zhang, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103631 · Cited by 33 · see citing works
Artificial intelligence for medicine 2025: Navigating the endless frontier
Dai, J., Xu, H., Chen, T., Huang, T., Liang, W., Zhang, R., Xu, G.-X., Zhang, Z., Xue, L., Gao, Y. Q., et al. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100120 · Cited by 35 · see citing works
Hou, J., Zhang, R., Xie, Y., Li, C., & Qin, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02257-y · Cited by 38 · see citing works
“In medicine, various foundation models handle different data modalities,suchasscFoundationforgenesequencingdata 45 ,UNIforwhole-slide images 46 , CONCH 24 and MUSK 47 for image-text pairs.”
Gao, P., Xiao, Q., Tan, H. J. J., Song, J., Fu, Y., Xu, J., Zhao, J., Miao, Y., Li, X., Yi, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101848 · Cited by 30 · see citing works
“All tiles underwent color normalization using Macenko’s 47,48 technique to mitigate color bias and streamline model training and assessment.”
Spatial omics enters the microscopic realm: opportunities and challenges
Si, Y., Lee, J. S., Jun, G., Kang, H. M., & Lee, J. H. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.05.002 · Cited by 31 · see citing works
Kludt, C., Wang, Y., Ahmad, W., Bychkov, A., Fukuoka, J., Gaisa, N. T., Kühnel, M., Jonigk, D. D., Pryalukhin, A., Mairinger, F. D., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101697 · Cited by 29 · see citing works
Ma, Y., Jamdade, S., Konduri, L., & Sailem, H. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01524-2 · Cited by 29 · see citing works
Gao, Y., Wen, P., Liu, Y., Sun, Y., Qian, H., Zhang, X., Peng, H., Gao, Y., Li, C., Gu, Z., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06428-z · Cited by 29 · see citing works
“PathChat was constructed by combining a specially trained pathological image visual encoder [61] with a pre-trained large language model (such as Llama 2), and fine-tuned on over 450,000 diverse image-text instructions (containing 999,202 rounds of Q&A).”
Application of artificial intelligence and digital tools in cancer pathology
Shaktah, L., Carrero, Z. I., Hewitt, K. J., Gustav, M., Cecchini, M. J., Foersch, S., Berezowska, S., & Kather, J. N. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100933 · Cited by 31 · see citing works
A foundation model for continuous glucose monitoring data
Lutsker, G., Sapir, G., Shilo, S., Merino, J., Godneva, A., Greenfield, J. R., Samocha‐Bonet, D., Dhir, R., Gude, F., Mannor, S., et al. (2026) · Nature · doi:10.1038/s41586-025-09925-9 · Cited by 49 · see citing works
“11–16).”
Xue, S., Zhu, F., Chen, J., & Min, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae630 · Cited by 28 · see citing works
“Moreover,scstGCN has exhibited its capability in enhancing spatial patterns of significant genes, discovering more biologically meaningful pathways and high-resolution annotation of tissue architecture.”
Xu, H., Wang, M., Shi, D., Qin, H., Zhang, Y., Liu, Z., Madabhushi, A., Gao, P., Cong, F., & Lu, C. Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103456 · Cited by 33 · see citing works
“After that, we identified ImageNet pre-trained ResNet50 [23] and five recent histological foundation models: CTransPath [10], PathoDuet [11], PLIP [12], CONCH [13], and UNI [14], for conduct-163 ing patch-level embedding (see Fig.”
InfluentialMultimodal foundation model and benchmark for comprehensive retinal OCT image analysis
Morano, J., Fazekas, B., Sükei, E., Fecso, R., Emre, T., Gumpinger, M., Faustmann, G., Oghbaie, M., Schmidt-Erfurth, U. M., & Bogunović, H. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01852-3 · Cited by 31 · see citing works
“Similar to previous work on FMs 27,32,42,44,79,80 , all models were evaluated using linear probing on the target datasets.”
Multistain Pretraining for Slide Representation Learning in Pathology
Jaume, G., Vaidya, A., Zhang, A., H. Song, A., J. Chen, R., Sahai, S., Mo, D., Madrigal, E., Phi Le, L., & Mahmood, F. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-73414-4_2 · Cited by 27 · see citing works
Self-citationA comprehensive AI model development framework for consistent Gleason grading
Huo, X., Ong, K. H., Lau, K. W., Golé, L., Young, D. M., Tan, C. L., Zhu, X., Zhang, C., Zhang, Y., Li, L., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00502-1 · Cited by 27 · see citing works
“Addressing these limitations and designing comprehensive AI diagnostic model development framework that encompass diverse datasets 29 , ef fi cient annotation schemes, and consideration of model updateability are crucial steps towards advancing AI-assisted pathology diagnostic models and…”
InfluentialSelf-citationMahbod, A., Saeidi, N., Hatamikia, S., & Woitek, R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110571 · Cited by 33 · see citing works
“(pathology) Vision/Language 512 UNI [31] Med.”
STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images
Huang, T., Liu, T., Babadi, M., Ying, R., & Jin, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02020-3 · Cited by 28 · see citing works
“STPath also enhances pathology foundation models in weakly supervised settings, improving survival prediction by 6.1% for UNI and 5.6% for Gigapath, and gene mutation prediction by 5.6% for UNI and 5.0% for Gigapath among eight datasetsandninebiomarkers.”
InfluentialSchuiveling, M., Liu, H., Eek, D., Breimer, G. E., Suijkerbuijk, K. P. M., Blokx, W. A. M., & Veta, M. (2025) · GigaScience · doi:10.1093/gigascience/giaf011 · Cited by 26 · see citing works
A multimodal dataset for precision oncology in head and neck cancer
Dörrich, M., Balk, M., Heusinger, T., Beyer, S., Mirbagheri, H., Fischer, D. J., Kanso, H., Matek, C., Hartmann, A., Iro, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62386-6 · Cited by 26 · see citing works
“The resulting image-like embeddings were used to train a Convolutional Neural Network (CNN, see Figure 4) to the same task as in Figure 3.”
HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings
Tripathi, A. G., Waqas, A., Schabath, M. B., Yılmaz, Y., & Rasool, G. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02003-4 · Cited by 37 · see citing works
“For WSIs embedding generation, HONeYBEE supports UNI (ViT-L/ 16, 307M parameters), UNI2-h (ViT-g/14, 632M parameters), and Virchow2(DINOv2, 1.1B parameters) 16,27 .”
Loeffler, C. M. L., Nahhas, O. S. M. E., Muti, H. S., Carrero, Z. I., Seibel, T., van Treeck, M., Çifçi, D., Gustav, M., Bretz, K., Gaisa, N. T., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-02022-9 · Cited by 38 · see citing works
“To ensure the robustness of our findings, we also performed the same experiments using another pretrained vision transformers encoder called UNI [47], https:// github. com/ mahmo odlab/ UNI), followed by a transformer-based multi-head self-attention DL-model as previously already published [48]…”
MethodHuang, Q., Wu, S., Ou, Z., & Gao, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.004 · Cited by 27 · see citing works
Pan‐cancer analysis shapes the understanding of cancer biology and medicine
Cen, X., Lan, Y., Zou, J., Chen, R., Hu, C., Tong, Y., Zhang, C., Chen, J., Wang, Y., Zhou, R., et al. (2025) · 癌症：英文版 · doi:10.1002/cac2.70008 · Cited by 27 · see citing works
“Similar examples include recently developed foundation models for histopathology imaging [57, 58].”
Self-citationTiwari, A., Ghose, A., Hasanova, M., Faria, S. S., Mohapatra, S., Adeleke, S. M., & Boussios, S. (2025) · Discover Oncology · doi:10.1007/s12672-025-02212-z · Cited by 31 · see citing works
“Chen et al. [88] introduced a general-purpose foundation model trained on 100 million images from over 100,000 diagnostic H&E-stained WSIs across 20 major tissue types.”
A large model for non-invasive and personalized management of breast cancer from multiparametric MRI
Luo, L., Wu, M., Li, M., Xin, Y., Wang, Q., Vardhanabhuti, V., Chu, C. W., Li, Z., Zhou, J., Rajpurkar, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58798-z · Cited by 28 · see citing works
“The model was developed and extensively evaluated based on a large-scale multiparametric breast MRI dataset collected from three hospitals in China, achieving comparable malignancy differentiating performance with National Health Commission (NHC)-qualified radiologists.”
Training a high-performance retinal foundation model with half-the-data and 400 times less compute
Engelmann, J., & Bernabéu, M. O. (2025) · Nature Communications · doi:10.1038/s41467-025-62123-z · Cited by 31 · see citing works
“First, they train models from scratch using randomly initialised weights, which is particularly challenging and computationally expensive for modern vision transformer architectures 27 that do not have strong inductive biases like traditional convolutional neural networks.”
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
Chen, Y., Wang, G., Ji, Y., Li, Y., Ye, J., Li, T., Hu, M., Yu, R., Qiao, Y., & He, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00484 · Cited by 26 · see citing works
“SlideChat can generate comprehensive descriptions of whole-slide images and provide contextually relevant responses across various applications. momentum, mainly driven by breakthroughs in the visual foundation model [9, 28, 31].”
High-Parameter Spatial Multi-Omics through Histology-Anchored Integration
Liu, Y.-H., Wang, C.-Y., Wang, Z.-K., Chen, L., Li, Z., Song, J.-N., Zou, Q., Gao, R., Qian, B.-Z., Feng, X., et al. (2025) · bioRxiv · doi:10.1038/s41592-025-02926-6 · Cited by 32 · see citing works
“The model processes input H&E images through cell segmentation 45 and cropping to generate single-cell morphology images, which are then transformed into high-dimensional feature representations using a pretrained H&E foundation model 46 (Fig.”
- 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 · 2023: 4
- 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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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