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A visual-language foundation model for computational pathology
Open accessAbstract
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model’s usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image–caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. Developed using diverse sources of histopathology images, biomedical text and over 1.17 million image–caption pairs, evaluated on a suite of 14 diverse benchmarks, a visual-language foundation model achieves state-of-the-art performance on a wide array of clinically relevant pathology tasks.
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“Similarly, given a diagnostically challenging WSI, retrieving a similar WSI via slide search5,46–53 or pathology reports through cross-modal report search10,54–56 typically requires specialized algorithms to bridge the gap between fine-grained patch embeddings and slide-level information, introducing hurdles to clinical adoption.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [10]
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
“The choice of CONCHv1.5 for feature extraction was due to the fact that the model was pretrained on histology regions with diverse stains and tissue types, including FFPE, frozen tissue and IHC, thereby yielding region features that are robust against diverse tissue processing protocols.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [10]
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.
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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 [10]
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
“enabling it to create effective representations for non–H&E (hematoxylin and eosin)-stained images, such as immunohistochemistry and special stains, without relying on large public histology slide collections such as The Cancer Genome Atlas, Pancreatic Cancer AI Platform, and Genotype-Tissue Expression.”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [141]
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
“CONCH is a vision-language M-LLM specifically designed for computational histopathology.”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [141]
Experimental — accuracy not yet measured
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“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 [34]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Following the success of previous studies10, we use CoCa95, a state-of-the-art visual-language foundation model pretraining method, for both stages of vision-language alignment.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Contrastive vision language models in pathology, such as PLIP12 and CONCH13, align text and images at the patch-level.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [13]
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“They are zero-shot learners, i.e., they can solve downstream tasks for which they have not been trained (e.g., cancer subtyping).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [13]
The cited source says what the claim attributes to it.
In the citing paper
“It is pretrained on the largest histopathology-specific vision-language data set”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [141]
The cited source says what the claim attributes to it.
In the citing paper
“PLIP [194] and CONCH [195] were pretrained on paired pathology images and text descriptions using contrastive learning methods.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [195]
In the citing paper
“Several studies have developed visual–language foundation models using paired histopathology images and captions11,12.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [11]
In the citing paper
“Existing dual-modality foundation models in computational biology11,12 primarily combine images with textual descriptions, proving their utility in histopathology annotation and analysis.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [11]
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HyperPath: Knowledge-Guided Hyperbolic Semantic Hierarchy Modeling for WSI Analysis
Huang, P., Huang, Y., Zhao, W., He, J., & Yu, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_25 · Cited by 5
Zhao, H., Tao, D., Zhan, Y., Ni, J. F., & Chen, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3620443 · Cited by 6
“Finally, although CONCH [59] possesses an excellent text encoder, its 512-dimensional visual features are half the size of UNI’s, leading to a drop in performance.”
Histolytics: A panoptic spatial analysis framework for interpretable histopathology
Lehtonen, O., Nordlund, N. K., Salloum, S., Kalliala, I. E. J., Virtanen, A., & Hautaniemi, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.022 · Cited by 5
Novel concepts of cell-of origin in neuroendocrine neoplasms
Marinoni, I., Avanthay, S., & Alcala, N. (2025) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-025-04311-2 · Cited by 5
PASB: Pathology-aware Schrödinger bridge for virtual immunohistochemical staining
Qiu, F., (608493), Y. Z., Huang, Z., Zhu, X., & Wang, Z. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103869 · Cited by 7
AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue
Sakaguchi, M., Yoshizawa, A., Masui, K., Sakai, T., & Komori, T. (2025) · Cancers · doi:10.3390/cancers18010009 · Cited by 7
“For instance, visual-language foundation models (VLFMs) like CONCH integrate pathology reports (text) with images during pre-training [83].”
InfluentialPredicting lymph node metastasis in colorectal cancer using case-level multiple instance learning
Zou, L., Wang, X.-B., Li, J.-W., Ouyang, X., Luo, Y.-Y., Luo, Y., & Wang, C.-L. (2026) · World Journal of Gastroenterology · doi:10.3748/wjg.v32.i1.112090 · Cited by 6
McConnell, N., Vasudev, P., Yamada, D., Cheng, D., Azimbagirad, M., McCabe, J., Aslani, S., Shahin, A. H., Zhou, Y., Janes, S. M., et al. (2026) · Communications Medicine · doi:10.1038/s43856-025-01328-1 · Cited by 7
Learning with less: A survey of deep learning in medical imaging under varying supervision levels
Kumari, S., & Singh, P. P. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103375 · Cited by 7
Tackling the complexity of cancer with generative models
Conard, A. M., Hughes, M., Hall, J., Tenenholtz, N., Zimmermann, E., Crawford, L. A., Amini, A. P., & Severson, K. (2026) · Cell · doi:10.1016/j.cell.2026.03.027 · Cited by 5
Roy, A., & Verma, N. (2025) · Metabolism and Target Organ Damage · doi:10.20517/mtod.2024.84 · Cited by 5
Computational pathology for breast cancer: Where do we stand for prognostic applications?
Gessain, G., & Lacroix‐Triki, M. (2025) · The Breast · doi:10.1016/j.breast.2025.104464 · Cited by 5
Tampu, I. E., Nyman, P. O., Spyretos, C., Blystad, I., Shamikh, A., Prochazka, G., de Ståhl, T. D., Sandgren, J., Lundberg, P., & Haj‐Hosseini, N. (2025) · Brain Pathology · doi:10.1111/bpa.70029 · Cited by 7
“…(ViT) foundation models model pre-trained on more than 100M H&E histology patches from WSIs across 20 major tissue types using the self-supervised DINOv2 framework [29] and (3) CONCH [18], a vision-language foundation model pre-trained on more than 1.17M H&E histology image-caption pairs.”
Zhong, L., Huang, Z., Liu, Y., Liao, W., Zhang, S., Wang, G., & Zhang, S. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3595111 · Cited by 13
“Similarly, CONCH [12] and Quilt-1M [23] were trained on a dataset exceeding 1 million image-caption pairs using a contrastive loss for optimization.”
Interactive Medical Image Analysis with Concept-based Similarity Reasoning
Huy, T. D., Tran, S. K., Nguyen, P., Tran, N. H. K., Sam, T. B., van den Hengel, A., Liao, Z., Verjans, J. W. H., To, M., & Phan, V. M. H. (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/cvpr52734.2025.02868 · Cited by 5
“Despite the success of deep neural networks in medical image analysis [8, 9, 14, 22, 24, 29], their “black-box” nature limits their adoption in safety-critical clinical settings.”
Performance of o1 pro and GPT-4 in Self-Assessment Questions for Nephrology Board Renewal
Noda, R., Yuasa, C., Kitano, F., Ichikawa, D., & Shibagaki, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1702668 · Cited by 5
LazySlide: accessible and interoperable whole-slide image analysis
Zheng, Y., Abila, E., Chrenková, E., Buljan, I., Winkler, J., & Rendeiro, A. F. (2026) · Nature Methods · doi:10.1038/s41592-026-03044-7 · Cited by 10
“The framework uses state-of-the-art pathology-specific models, such as PLIP 29 and CONCH 30 , to facilitate semantic search within WSIs.”
Yu, Y., Wang, J., Liu, W., Mien, I. H., Krishnaswamy, P., Yang, X., & Cheng, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130018 · Cited by 5
Xu, J., Shi, L., Li, S., Zhang, Y., Zhao, G., Shi, Y., Li, J., & Gao, Y. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3565184 · Cited by 5
“CONCH [19] represents a substantial leap over concurrent visual-language pre-trained systems for histopathology, improving the practical value of general-purpose recognition or retrieval systems for histopathology.”
Dual-masked contrastive learning based hypergraph foundation model for whole slide images
Zhou, X., Ding, S., Zhang, W., Li, J., Wang, J., Chen, J., & Shi, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111995 · Cited by 5
Unifying the Electron Microscopy Multiverse through a Large-scale Foundation ModelPreprint
He, L., Shi, R., Wang, W., Fang, G., Cai, Y., & Ма, Л. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.13.648639 · Cited by 5
A large-scale curated and filterable dataset for cryo-EM foundation model pre-training
Chen, Q., Xu, Z., Dai, H., Shen, Y., Zhang, J., Liu, Z., Pei, Y., & Jingyi Yu, A. (2025) · Scientific Data · doi:10.1038/s41597-025-05179-2 · Cited by 7
“These models have been widely applied to various medical imaging modalities, including CT 49,50 , X-ray 51 , ultrasound 52 , and digital pathology 53–61 , significantly improving downstream task performance.”
Park, J., Gregorio, R. D., Hissong, E., Ozcelik, E., Bartelo, N., Dezem, F. S., Zhang, L., Marção, M., Chasteen, H., Zheng, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.16.658716 · Cited by 5
OmicsNavigator : An auditable scientific partner for scalable hypothesis validation in spatial omicsPreprint
Yiyao, L., Vakharia, N., Liang, W., Mayer, A. T., Luo, R., Trevino, A. E., & Wu, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.21.665821 · Cited by 5
Prompt injection attacks on vision-language models for surgical decision support
Zhang, Z., Qadir, M. I., Carstens, M., Zhang, E. H., Loiselle, M. S., Martinus, F. M., Mroczkowski, M. K., Clusmann, J., Kather, J. N., & Kolbinger, F. R. (2026) · npj Digital Surgery · doi:10.1038/s44484-026-00014-6 · Cited by 4
A position-aware sets based weakly supervised framework for whole-slide subtype classification
Song, J., Yu, B., Liu, X., Cong, L., Zhou, Z., Cong, X., Sun, H., Pang, S., & Chen, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111998 · Cited by 4
Bai, B., & Miyata, K. (2025) · IEEE Access · doi:10.1109/access.2025.3609221 · Cited by 4
Using spatial proteomics to enhance cell type assignments in histology images
Dayao, M. T., Mayer, A. T., Trevino, A. E., & Bar‐Joseph, Z. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101204 · Cited by 4
Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning
Lou, Y., Su, Y., Lu, H., Li, W., Yin, W., Li, S., Zhu, H., Ong, K. H., Chen, G., Jiang, Y., et al. (2025) · Advanced Science · doi:10.1002/advs.202511369 · Cited by 5
Fischer, M., Muckenhuber, A., Peretzke, R., Farah, L., Ulrich, C., Ziegler, S., Schader, P., Feineis, L., Gao, H., Xiao, S., et al. (2025) · The Journal of Pathology · doi:10.1002/path.6491 · Cited by 6
“Therefore, automatic computational methods for tumor subtyping have emerged as promising approaches to overcome limitations associated with manual pathological assessments [5 – 9].”
Li, J., Sun, Q., Yan, R., Wang, Y., Fu, Y., Wei, Y., Guan, T., Shi, H., He, Y., & Han, A. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103894 · Cited by 11
Wu, X., Wang, F., Dai, W., Ni, C., Sun, L., Gong, Y., Dong, N., Wang, Z., Li, L., Xu, Q., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01224-w · Cited by 4
“In medical imaging applications, ViT-based frameworks including UNI, CONCH, and TITAN have made substantial progress in critical pathological tasks such as tissue classi fi cation and tumor detection 26 – 28 .”
Luo, H., Huang, J., Zhou, T., Wu, E., & Ding, W. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3609687 · Cited by 5
“Moreover, recent pathology-specific models like PLIP [45], CONCH [46], UNI [47], and mSTAR [48] have demonstrated superior feature extraction capabilities compared to ResNet50 pretrained on ImageNet.”
Shi, Z., Xue, S., & Min, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112647 · Cited by 4
UniCAS: A foundation model for cervical cytology screening
Jiang, H., Cai, J., Shen, Z., Xu, M., Fei, M., Huang, H., Wang, X., Bi, R., Shen, D. G., Zhang, L., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102570 · Cited by 5
Hezil, N., Bouridane, A., Hamoudi, R., Al-Maadeed, S. A., Akbari, Y., & Abdullakutty, F. C. (2026) · Medical Engineering & Physics · doi:10.1088/1873-4030/ae449b · Cited by 6
Foundation models in medical imaging: A review
Chen, Q., Liu, A., Zhang, J., Yang, C., & Zhang, Y. (2026) · EngMedicine · doi:10.1016/j.engmed.2026.100123 · Cited by 8
Huang, H., Wu, K., Qu, L., Sun, X.-D., Li, M., Wei, F., Zhang, P., Kow, A. W. C., Chen, Y., Jin, M., et al. (2026) · Gut · doi:10.1136/gutjnl-2025-337945 · Cited by 4
“5, pretrained on millions of histopathology images, 20 extracts tissue patches as high-dimensional semantic tokens rather than reductionist scalar ratios.”
Silberman, N., Parwani, A. V., McClintock, D. S., Lujan, G. M., Gondim, D. D., Garcia, C., Hanna, M. G., Pantanowitz, L., Lennerz, J. K., Showalter, T. N., et al. (2026) · AI in Precision Oncology · doi:10.1177/2993091x261455975 · Cited by 5
Self-citationYadav, A., Alvarez, K., Chechenina, A., Yip, K. Y.-L., Ruppin, E., Colas, A. R., Maher, J. Y., Gomez‐Lobo, V., Kumsta, C., & Sinha, S. (2026) · Nature Aging · doi:10.1038/s43587-026-01200-4 · Cited by 6
“5 40 and Virchow2 41 ) reproduced overall similar structural patterns across most tissues (Supplementary Figs.”
MFMF: Multiple Foundation Model Fusion Networks for Whole Slide Image Classification
Dang, T. M., Guo, Y., Ma, H., Zhou, Q., Na, S., Gao, J., & Huang, J. (2024) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3698587.3701372 · Cited by 4
“Intheunimodal setting, the input to baselines is a set of patch feature vectors F extracted using the foundation CONCH model.”
InfluentialALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering
Gao, Z., He, K., Su, W., Pang, X., Machado, I. P., Jimenez‐Liñan, M., Rous, B., Wang, C., Li, C., McGough, W., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76372-z · Cited by 6
“ALPaCA leverages frozen CONCH 42 , a vision encoder pre-trained on 1.17 million pathology image-caption pairs, to extract hierarchical patch embeddings.”
InfluentialSelf-citationDeep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma
Naji, H., Hahn, P., Pisula, J. I., Ugliano, S., Simon, A., Büttner, R., & Bożek, K. (2025) · BJC Reports · doi:10.1038/s44276-025-00147-0 · Cited by 5
Hu, X., Cao, Y., Hu, W., Wang, S., Mukhopadhyay, S. C., Liu, Y., Li, H., Li, Y., Cai, Q., & Liu, Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103432 · Cited by 4
Now and future of artificial intelligence-based signet ring cell diagnosis: A survey
Zhu, M. S., Dong, J., Guo, L., Su, F., Liu, J., Wang, G., & Zhao, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129188 · Cited by 6
AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides
Innani, S., Bell, W. R., Nasrallah, M. P., Baheti, B., & Bakas, S. (2025) · Neuro-Oncology · doi:10.1093/neuonc/noaf189 · Cited by 6
MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Xiang, T., Li, Y., Zhang, Q., & Li, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_40 · Cited by 4
PathBot: A Foundation Model for Pathological Image Analysis
Lu, M., Wang, T., Zeng, Q., Lu, Z., Li, Z., & Xia, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3619967 · Cited by 4
“Vision-language models such as PLIP [36] and CONCH [37] incorporate textual inputs.”
InfluentialSelf-citationLarge Language Models for Translational Cancer Informatics
Pan, Y., Wang, Y., Wang, G., Su, J., Topaloĝlu, Ü., & Song, Q. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00108 · Cited by 5
His‐MMDM: Multi‐Domain and Multi‐Omics Translation of Histopathological Images with Diffusion Models
Li, Z., Su, T., Zhang, B., Han, W., Zhang, S., Sun, G., Cong, Y., Chen, X., Qi, J., Wang, Y., et al. (2026) · Advanced Science · doi:10.1002/advs.202518066 · Cited by 7
Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images
Han, M., Zhang, X., Yang, D., Liu, T., Kuang, H., Feng, J., & Zhang, L. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10688366 · Cited by 5
“In clinical practice, survival prediction is commonly conducted by analyzing patients’ Whole Slide Images (WSIs), which serve as the “gold standard” for cancer diagnosis, staging, and survival prediction [7]–[10].”
MethodExploring Foundation Models Fine-Tuning for Cytology Classification
Dausort, M., Godelaine, T., Zanella, M., Khoury, K. E., Salmon, I. J., & Macq, B. M. M. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981249 · Cited by 4
Lu, X.-J., Ying, Y., Chen, J., Chen, Z., Wu, Y., Prasanna, P., Chen, X., Jing, M., Liu, Z., & Lu, C. Y. (2025) · Brain‐X · doi:10.1002/brx2.70030 · Cited by 5
AI-driven Discovery of Morphomolecular Signatures in ToxicologyPreprint
Jaume, G., Peeters, T., Song, A. H., Pettit, R. W., Williamson, D. F. K., Oldenburg, L., Vaidya, A. J., de Brot, S., Chen, R. J., Thiran, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.19.604355 · Cited by 4
Self-citationecPath detects ecDNA in tumors from histopathology imagesPreprint
Choudhury, M., Liu, L., Yadav, A., Chapman, O. S., Ahmadi, Z., Younis, R., Sharma, C., Goel, N., Sridhar, S., Kenkre, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.13.623494 · Cited by 4
Metadata
- Authors
- Lu, M. Y., Chen, B., Williamson, D. F. K., Chen, R. J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L. P., Gerber, G., Parwani, A. V., Zhang, A., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- The Ohio State University Wexner Medical Center (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-03
- Volume
- 30
- Issue
- 3
- Pages
- 863-874
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11384335
- OpenAlex
- W4392947532
- Semantic Scholar
- 0746410e1250576cc1b9621ef602e4bceb1c07ca
- Cited by
- 966
- References
- 79
- Citations by year
- 2026: 491 · 2025: 388 · 2024: 87
- FWCI
- 135.39
- 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, R35GM138216)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK034854)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- histopathology, digital pathology, contrastive learning, image-caption pairs, biomedical text, histology image classification, image captioning, text-to-image retrieval, image-to-text retrieval, transfer learning, limited labeled data
- MeSH terms
- Machine Learning, Humans, Language, Workflow
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