The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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
The 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 [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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Measures of the Amount of Ecologic Association Between Species
Dice, L. R. (1945) · Ecology · doi:10.2307/1932409 · Cited by 12,187
Learning to Prompt for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01653-1 · Cited by 3,304
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282
Self-citationScaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., & Duerig, T. (2021) · arXiv · doi:10.48550/arxiv.2102.05918 · Cited by 1,977
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., & Qiao, Y. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01891-x · Cited by 1,589
Flamingo: a Visual Language Model for Few-Shot Learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.14198 · Cited by 1,555
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A. S., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101563 · Cited by 1,516
CoCa: Contrastive Captioners are Image-Text Foundation ModelsPreprint
Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., & Wu, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.01917 · Cited by 1,413
BioGPT: generative pre-trained transformer for biomedical text generation and mining
Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., & Liu, T.-Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac409 · Cited by 1,150
Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082
LAION-5B: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.08402 · Cited by 1,022
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
Wang, Z., Wu, Z., Agarwal, D., & Sun, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.256 · Cited by 1,011
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777
Self-citationTransformer-based unsupervised contrastive learning for histopathological image classification
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102559 · Cited by 751
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705
Generating Radiology Reports via Memory-driven Transformer
Chen, Z., Song, Y., Chang, T.-H., & Wan, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.112 · Cited by 693
A visual–language foundation model for pathology image analysis using medical twitter
Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J., & Zou, J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02504-3 · Cited by 693
Pan-cancer image-based detection of clinically actionable genetic alterations
Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C. M. L., Echle, A., Muti, H. S., Krause, J., Niehues, J., Sommer, K. A. J., Bankhead, P., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0087-6 · Cited by 686
FLAVA: A Foundational Language And Vision Alignment Model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., & Kiela, D. (2022) · 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/cvpr52688.2022.01519 · Cited by 678
Revisiting Self-Supervised Visual Representation Learning
Kolesnikov, A., Zhai, X., & Beyer, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00202 · Cited by 675
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660
Self-citationPan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Fu, Y., Jung, A. W., Viñas, R., González, S., Vöhringer, H. S., Shmatko, A., Yates, L. R., Jimenez‐Liñan, M., Moore, L., & Gerstung, M. (2020) · Nature Cancer · doi:10.1038/s43018-020-0085-8 · Cited by 655
Multi-omic machine learning predictor of breast cancer therapy response
Sammut, S.-J., Crispin-Ortuzar, M., Chin, S.-F., Provenzano, E., Bardwell, H. A., Ma, W., Cope, W., Dariush, A., Dawson, S.-J., Abraham, J. E., et al. (2022) · Nature · doi:10.1038/s41586-021-04278-5 · Cited by 654
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Bulten, W., Kartasalo, K., Chen, P.-H. C., Ström, P., Pinckaers, H., Nagpal, K., Cai, Y., Steiner, D. F., van Boven, H., Vink, R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01620-2 · Cited by 640
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636
Self-citationGIT: A Generative Image-to-text Transformer for Vision and LanguagePreprint
Wang, J., Yang, Z., Hu, X., Li, L., Lin, K., Gan, Z., Liu, Z., Liu, C., & Wang, L. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.14100 · Cited by 636
Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(19)32998-8 · Cited by 627
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612
Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasks
Wang, W., Bao, H., Dong, L., Bjorck, J., Peng, Z., Liu, Q., Aggarwal, K., Mohammed, O. K., Singhal, S., Som, S., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01838 · Cited by 561
Deep learning-based classification of mesothelioma improves prediction of patient outcome
Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., Manceron, P., Toldo, S., Zaslavskiy, M., Le Stang, N., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0583-3 · Cited by 544
Gatta, G., Capocaccia, R., Botta, L., Mallone, S., De Angelis, R., Ardanaz, E., Comber, H., Dimitrova, N., Leinonen, M. K., Siesling, S., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30445-x · Cited by 516
Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495
Huang, S.-C., Shen, L., Lungren, M. P., & Yeung, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00391 · Cited by 489
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446
Tiu, E., Talius, E., Patel, P., Langlotz, C. P., Ng, A. Y., & Rajpurkar, P. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00936-9 · Cited by 442
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
Chen, R. J., Lu, M. Y., Weng, W.-H., Chen, T. Y., Williamson, D. F., Manz, T., Shady, M., & Mahmood, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00398 · Cited by 398
Self-citationVanguri, R. S., Luo, J., Aukerman, A. T., Egger, J. V., Fong, C. J., Horvat, N., Pagano, A., Araujo-Filho, J. D. A. B., Geneslaw, L., Rizvi, H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00416-8 · Cited by 395
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
Wouter, B., Kimmo, K., Cameron, C. P.-H., Peter, S., Hans, P., Kunal, N., Yuannan, C., Steiner, D. F., van Boven, H., Robert, V., et al. (2022) · UTUPub (University of Turku) · Cited by 375
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical DocumentsPreprint
Lin, W.-X., Zhao, Z.-H., Zhang, X., et al. (2023) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.48550/arxiv.2303.07240 · Cited by 363
Artificial intelligence for digital and computational pathology
Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356
Self-citationWei, J., Tafe, L. J., Linnik, Y., Vaickus, L. J., Tomita, N., & Hassanpour, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-40041-7 · Cited by 323
Scaling Language-Image Pre-Training via Masking
Li, Y., Fan, H., Hu, R., Feichtenhofer, C., & He, K. (2023) · 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/cvpr52729.2023.02240 · Cited by 292
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T., Williamson, D. F. K., Zhao, M. M., Shady, M., Lipková, J., & Mahmood, F. (2021) · RePEc: Research Papers in Economics · Cited by 263
Self-citationMultistain deep learning for prediction of prognosis and therapy response in colorectal cancer
Foersch, S., Glasner, C., Woerl, A.-C., Eckstein, M., Wagner, D.-C., Schulz, S., Kellers, F., Fernandez, A., Tserea, K., Kloth, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02134-1 · Cited by 251
PMC-CLIP: Contrastive Language-Image Pre-training Using Biomedical Documents
Lin, W., Zhao, Z., Zhang, X., Wu, C., Zhang, Y., Wang, Y., & Xie, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_51 · Cited by 249
Riasatian, A., Babaie, M., Maleki, D., Kalra, S., Valipour, M., Hemati, S., Zaveri, M., Safarpoor, A., Shafiei, S., Afshari, M., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102032 · Cited by 221
RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval
Wang, X., Du, Y., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102645 · Cited by 218
Clinically Accurate Chest X-Ray Report GenerationPreprint
Liu, G., Hsu, T.-M. H., McDermott, M. B. A., Boag, W., Weng, W., Peter Szolovits, B., & Ghassemi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02633 · Cited by 212
Similar image search for histopathology: SMILY
Hegde, N., Hipp, J. D., Liu, Y., Emmert-Buck, M., Reif, E., Smilkov, D., Terry, M., Cai, C. J., Amin, M. B., Mermel, C. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0131-z · Cited by 157
OncoTree: A Cancer Classification System for Precision Oncology
Kundra, R., Zhang, H., Sheridan, R., Sirintrapun, S. J., Wang, A., Ochoa, A., Wilson, M., Gross, B. E., Sun, Y., Madupuri, R., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00108 · Cited by 150
Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101757 · Cited by 139
Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130
Self-citationVisual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Lu, M. Y., Chen, B., Zhang, A., Williamson, D. F. K., Chen, R. J., Ding, T., Le, L. P., Chuang, Y.-S., & Mahmood, F. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01893 · Cited by 122
Self-citationCLIP-Adapter: Better Vision-Language Models with Feature AdaptersPreprint
Gao, P., Geng, S., Zhang, R., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04544 · Cited by 113
Da, Q., Huang, X., Li, Z., Zuo, Y., Zhang, C., Liu, J., Chen, W., Li, J., Xu, D., Hu, Z., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102485 · Cited by 103
Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R. P. N., Wei, M., Valluri, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.00915 · Cited by 102
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosisPreprint
Fu, Y., Jung, A. W., Viñas, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/813543 · Cited by 95
Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
Saldanha, O. L., Loeffler, C. M. L., Niehues, J., van Treeck, M., Seraphin, T. P., Hewitt, K. J., Çifçi, D., Veldhuizen, G. P., Ramesh, S., Pearson, A. T., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00365-0 · Cited by 92
Huang, Z., Shao, W., Han, Z., Alkashash, A. M., la Sancha, C. D., Parwani, A. V., Nitta, H., Hou, Y., Wang, T., Salama, P., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00352-5 · Cited by 90
Self-citationPan-cancer image-based detection of clinically actionable genetic alterationsPreprint
Kather, J. N., Heij, L. R., Grabsch, H. I., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/833756 · Cited by 80
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
Gamper, J., & Rajpoot, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01628 · Cited by 75
The Digital Brain Tumour Atlas, an open histopathology resource
Roetzer-Pejrimovsky, T., Moser, A., Atli, B., Vogel, C., Mercea, P. A., Prihoda, R., Gelpí, E., Haberler, C., Höftberger, R., Hainfellner, J. A., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01157-0 · Cited by 74
Vision-Language Transformer for Interpretable Pathology Visual Question Answering
Naseem, U., Khushi, M., & Kim, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3163751 · Cited by 68
Towards Visual Question Answering on Pathology Images
He, X., Cai, Z., Wei, W., Zhang, Y., Mou, L., Xing, E. P., & Xie, P. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-short.90 · Cited by 66
Schaumberg, A. J., Juarez-Nicanor, W. C., Choudhury, S. J., Guerra, L., Pritt, B. S., Pozuelo, M. P., Sánchez, R. S., Ho, K., Zahra, N., Sener, B. D., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0540-1 · Cited by 56
Quilt-1M: One Million Image-Text Pairs for Histopathology
Ikezogwo, W. O., Seyfioglu, M. S., Ghezloo, F., Geva, D. S. C., Mohammed, F. S., Anand, P. K., Krishna, R., & Shapiro, L. (2023) · arXiv · doi:10.48550/arxiv.2306.11207 · Cited by 55
Self-Learning for Weakly Supervised Gleason Grading of Local Patterns
Silva-Rodríguez, J., Colomer, A., Dolz, J., & Naranjo, V. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3061457 · Cited by 47
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
Li, H., Zhu, J., Jiang, X., Zhu, X., Li, H., Yuan, C., Wang, X., Qiao, Y., Wang, X., Wang, W., et al. (2023) · 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/cvpr52729.2023.00264 · Cited by 41
Zhu, L., Shi, H., Wei, H., Wang, C., Shi, S., Zhang, F., Yan, R., Liu, Y., He, T., Wang, L., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104426 · Cited by 40
FLAVA: A Foundational Language And Vision Alignment ModelPreprint
Singh, A., Hu, R., Goswami, V., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.04482 · Cited by 36
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung AdenocarcinomaPreprint
Han, C., Pan, X., Yan, L., Lin, H., Li, B., Yao, S., Lv, S., Shi, Z., Mai, J., Lin, J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.06455 · Cited by 27
Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide ImagesPreprint
Kalra, S., Choi, C. Q., Shah, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08748 · Cited by 19
PathNarratives: Data annotation for pathological human-AI collaborative diagnosis
Zhang, H., He, Y., Wu, X., Huang, P., Qin, W., Wang, F., Ye, J., Huang, X., Liao, Y., Chen, H., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1070072 · Cited by 19
When is a Foundation Model a Foundation ModelPreprint
Alfasly, S., Nejat, P., Hemati, S., Khan, J., Lahr, I., Alsaafin, A., Shafique, A., Comfere, N. I., Murphree, D. H., Meroueh, C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.11510 · Cited by 19
Riasatian, A., Babaie, M., Maleki, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07903 · Cited by 12
Visual 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-citationHuo, X., Ong, K. H., Lau, K. W., Golé, L., Tan, C. L., Zhang, C., Zhang, Y., Zhu, X., Li, L., Han, H., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4172090 · Cited by 6
The Digital Brain Tumour Atlas, an open histopathology resource
Roetzer-Pejrimovsky, T., Moser, A. C., Atli, B., Vogel, C., Mercea, P. A., Prihoda, R., Gelpí, E., Haberler, C., Höftberger, R., Hainfellner, J. A., et al. (2022) · EBRAINS · doi:10.25493/wq48-zgx · Cited by 5
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language TasksPreprint
Li, H., Zhu, J., Jiang, X., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.09808 · Cited by 3
Research works printed but not linked: 13
- Li, J., Li, D., Xiong, C. & Hoi, S. BLIP: bootstrapping language–image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning (eds Chaudhur, K. et al.) 12888–12900 (PMLR, 2022).
- Maleki, D. & Tizhoosh, H. R. LILE: look in-depth before looking elsewhere—a dual attention network using transformers for cross-modal information retrieval in histopathology archives. In International Conference on Medical Imaging with Deep Learning (eds Konukoglu, E. et al.) 879–894 (PMLR, 2022).
- Zhang, Y., Jiang, H., Miura, Y., Manning, C. D. & Langlotz, C. P. Contrastive learning of medical visual representations from paired images and text. In Machine Learning for Healthcare Conference (eds Lipton, Z. et al.) 2–25 (PMLR, 2022).
- Tsuneki, M. & Kanavati, F. Inference of captions from histopathological patches. In International Conference on Medical Imaging with Deep Learning (eds Konukoglu, E. et al.) 1235–1250 (PMLR, 2022).
- Zhang, R., Weber, C., Grossman, R. & Khan, A. A. Evaluating and interpreting caption prediction for histopathology images. In Machine Learning for Healthcare Conference (eds Doshi-Velez, F. et al.) 418–435 (PMLR, 2020).
- Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In International Conference on Machine Learning (eds Dy, J. & Krause, A.) 2127–2136 (PMLR, 2018).
- Perez, E., Kiela, D. & Cho, K. True few-shot learning with language models. Adv. Neural Inf. Process. Syst. 34, 11054–11070 (2021).
- Dosovitskiy, A. et al. An image is worth 16×16 words: transformers for image recognition at scale. In 9th International Conference on Learning Representations https://openreview.net/forum?id=YicbFdNTTy (OpenReview.net, 2021).
- Zhou, J. et al. Image BERT pre-training with online tokenizer. In 10th International Conference on Learning Representations https://openreview.net/forum?id=ydopy-e6Dg (OpenReview.net, 2022).
- Li, J., Li, D., Savarese, S. & Hoi, S. BLIP-2: bootstrapping language–image pre-training with frozen image encoders and large language models. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 19730–19742 (PMLR, 2023).
- Banerjee, S. & Lavie, A. METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In Proc. ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization 65–72 (Association for Computational Linguistics, 2005).
- Lin, C.-Y. ROUGE: a package for automatic evaluation of summaries. In Text Summarization Branches Out 74–81 (Association for Computational Linguistics, 2004).
- Lewis, M., Dauphin, Y. & Fan, A. Hierarchical neural story generation. In Proc. 56th Annual Meeting of the Association for Computational Linguistics (eds Gurevych, I. & Miyao, Y.) 889–898 (Association for Computational Linguistics, 2018).
Cited by
Sorted by citations
A 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
The TRIPOD-LLM reporting guideline for studies using large language models
Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G., Demner-Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03425-5 · Cited by 615
“Of note, this guideline was developed with text-only LLMs in mind; however, advances in multimodal models incorporating LLMs, such as vision-language models 47 , are now rapidly emerging — illustrating the need for rapid, nimble approaches for reporting guidelines.”
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-citationMultimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349
“In this section, we present a use case that demonstrates the practical application of M-LLMs in health care using the Contrastive Learning From Captions for Histopathology (CONCH) model [141].”
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
“A key feature of a foundation model is its ability to perform downstream tasks without further training, that is, zero-shot learning capability 15,16 .”
Influential
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
