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-citation
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
Items 501–600 of 968
Zhang, X., Wang, T., Yan, C., Najdawi, F., Zhou, K., Ma, Y., Cheung, Y.-M., Yeung, M. C. F., & Malin, B. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01694-8 · Cited by 1
“We further investigated four digital pathology foundation models that exemplify the recent advancements in large, general-purpose models in the field: (1) UNI 28 , (2) CONCH 29 , (3) Prov-GigaPath 30 and (4) TITAN 31 .”
InfluentialYe, Y., Yuan, J., Tang, J., Wan, P., Sun, L., Sheng, J., Zhang, D., & Shao, W. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3705155 · Cited by 1
“…of categories ( n c = | C s | + | C u | ), we utilize a frozen language encoder denoted as f text ( · ) from the pathology-language model CONCH [28] to e ff ectively map the descriptions of each tissue category into Authorized licensed use limited to the terms of the applicable license…”
InfluentialA roadmap for medical large language models: a review of foundations, applications, and challenges
Sha, Y.-Y., Yu, L., Lin, Z.-H., Kaur, A., Lou, Y., Gornale, S. S., Zhang, T., Wong, L. S., Wang, Z., Yan, Y., et al. (2026) · Military Medical Research · doi:10.1016/j.mmr.2026.100050 · Cited by 3
“These developments indicate a gradual shift from single-modality image analysis toward more integrated multimodal reasoning in medical AI [36,231].”
Mantle cell lymphoma artificial intelligence prognostic index using hematoxylin and eosin histology
Lippl, J., Reinke, S., Koch, K., Oschlies, I., Schrod, S., Wolfseher, L., Hüttl, P., Huttner, M., Beà, S., Campo, E., et al. (2026) · Leukemia · doi:10.1038/s41375-026-03036-8 · Cited by 1
“Whether other foundation models [35 – 38] bear the potential to increase the performance of MAIPI may be analyzed in future studies but was not the focus of our research.”
Razzaq, T., Taj, M., & Iqbal, A. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105075 · Cited by 1
Gao, Z., Ge, J., Lu, J., Wu, L., Clayton, H., Zhang, D., Liu, J., Machado, I. P., Chen, Y., Crispin‐Ortuzar, M., et al. (2026) · Intelligent Oncology · doi:10.1016/j.intonc.2026.100074 · Cited by 1
Next-generation kidney tissue analysis — spatial omics and digital pathology
Yoshikawa, T., Bülow, R. D., Boor, P., & Yanagita, M. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01102-z · Cited by 3
“Foundation models in computational pathology have been trained using image data 157 , or multimodal data sources including images and texts 158 .”
Akbasli, I. T., Oztürk, B., Serin, O., Doğan, V., Berikol, G. B., Comeau, D. S., Celi, L. A., & Ozguner, O. (2026) · Journal of medical imaging · doi:10.1117/1.jmi.13.4.045501 · Cited by 2
Unified multimodal computational pathology with missing-modality robustness via Riemannian learning
Yang, G., Qu, M., Di, D., Yi, K., Xu, H. J., Su, T., & Fan, L. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104633 · Cited by 1
Bao, W., Wang, T., Xia, X., Chen, L., Qi, H., Feng, Y., Fa, X., Ma, G., Chen, S., Leng, K., et al. (2026) · Translational Oncology · doi:10.1016/j.tranon.2026.102946 · Cited by 1
Magisson, F., He, Z., Millward, J., Harris, A., Chen, Z., Mielke, L. A., Tran, K., Ward, R. L., Hawkins, N. J., Sieber, O. M., et al. (2026) · Gastroenterology · doi:10.1053/j.gastro.2026.07.009 · Cited by 1
Dong, C., Liu, Y., Yin, J., Yan, W., Sun, J., Zhang, X., Huo, S., Yu, F., & Zhou, Y. (2026) · Digital Health · doi:10.1177/20552076261474637 · Cited by 3
“68,69”
Corso, F., Zec, A., Favali, M., Ligero, M., Hilgers, L., Invernizzi, L. M., Mazzeo, L., Morales, A. M., Müller‐Franzes, G. A., Machado, I. P., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102966 · Cited by 2
Rethinking Architectural Complexity in Deep Vision Models for Histopathological Image Classification
Abbas, Q., Liaqat, M. I., Qayum, S., & Hina, S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02175-9 · Cited by 1
Gao, G., Yan, R., Song, A. H., Hsieh, H.-C., Barner, L. A. E., Wang, F., Brenes, D., Chow, S. S. L., Wang, R., Bishop, K. W., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01760-1 · Cited by 7
Self-citationPractical applications of computational pathology: bridging technology and clinical practice
Hanna, M. G., Rashidi, H., & Pantanowitz, L. (2026) · Pathology · doi:10.1016/j.pathol.2026.06.012 · Cited by 1
Deep molecular profiling of lung neuroendocrine tumours and supra-carcinoids
Sexton‐Oates, A., Mathian, É., Candeli, N., Lim, Y., Voegele, C., Genova, A. D., Mangé, L., Li, Z., van Weert, T. J. J., Kalson, L., et al. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02721-7 · Cited by 4
“To test the generalisation of morphological features identified in tiles to entire whole slide images, we estimated the presence of two significant and easily recognisable features (spindle cells, and fibrotic tissue) using the visual-language model CONCH [65].”
Multi‐omics–driven precision medicine
Li, H., Zhao, Z., Zhang, Y., Ma, Y., Hu, G., Zeng, M., Yang, Z., Zhao, Z., Zhou, X., Hu, W., et al. (2026) · iMeta · doi:10.1002/imt2.70165 · Cited by 1
Yew, S. M. E., Lei, X., Goh, J. H. L., Zou, K., Chen, Y., Srinivasan, S., Chee, M.-L., Pushpanathan, K., Hou, Q., Soh, Z. D., et al. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.101031 · Cited by 2
Chen, Y., Ma, C., Li, Q., Yan, F., Chen, Y., Li, T., Ye, J., Hu, M., Lin, Y., Li, Y., et al. (2026) · Nature Cancer · doi:10.1038/s43018-026-01220-4 · Cited by 1
Ensemble learning of pathology foundation models for precision oncology
Luo, X., Wang, X., Eweje, F. R., Zhang, X., Marti, J. L. G., Cascarino, S., Yang, S., Li, Y., Quinton, R. J., Xiang, J., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.08.008 · Cited by 17
An expert-level generalist AI for abdominal CT diagnosis
Zhang, Q., Zhang, J., Cao, W., Lu, Z., Chang, W., Ding, H., Chen, C., Li, Z., Xue, X., Zhang, S., et al. (2026) · Science · doi:10.1126/science.aec6129 · Cited by 4
Melanoma Prognostication Using AI ‐Guided Histopathology
Lee, E., Karagenova, R., & Semenov, Y. R. (2026) · International Journal of Dermatology · doi:10.1111/ijd.70585 · Cited by 2
A Simplified Query-Only Attention for Encoder-Based Transformer Models
Yeom, H. G., & An, K. (2024) · Applied Sciences · doi:10.3390/app14198646 · Cited by 2
“Owing to their exceptional performance in NLP, transformers have been widely adopted across domains beyond text processing [20,26–29 , 31– 34].”
The puzzling Spitz tumours: is artificial intelligence the key to their understanding?
Launet, L., Colomer, A., Mosquera‐Zamudio, A., Monteagudo, C. C., & Naranjo, V. (2025) · Histopathology · doi:10.1111/his.15428 · Cited by 1
“By leveraging this broad training data, FMs like PLIP 50 and CONCH 51 have proven to be effective feature extractors.”
Yan, C., Ruan, Z., Chen, S., Pan, Y., Han, X., Li, Y., & Liu, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf201 · Cited by 1
“We also compared Kullback–Leibler divergence (KLD) and contrastive information noise contrast estimation (infoNCE) (St € ark et al. 2022, Lu et al. 2024) loss functions for cross-modal embedding alignment.”
Redekop, E., Pleasure, M., Ivezić, V., Wang, Z., Flores, K., Sisk, A. E. J., Speier, W. F., & Arnold, C. W. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00514 · Cited by 1
“…models against three baseline encoders, noting significant dataset size variations: UNI [7] (100 million images, ∼ 60x larger than iBOT-Synth), CONCH [19] (1.17 million image-caption pairs, comparable to iBOT-Synth), and Prov-GigaPath [27] (1.3 billion images, ∼ 760x larger than iBOT-Synth).”
InfluentialCoC: Chain-of-Cancer Based on Cross-Modal Autoregressive Traction for Survival Prediction
Zhou, H., Yang, S., Yang, S., Qin, J., Chen, L., & Zhu, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05182-0_9 · Cited by 1
Enhancing zero-shot brain tumor subtype classification via fine-grained patch-text alignment
Gan, L., Zhang, J., Qu, L., Wang, Y., Wu, S., & Sun, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130161 · Cited by 3
Prompt-guided foundation model tuning for pathology image classification
Lin, Y., Zhu, Z., Cheng, K.-T. T., & Chen, H. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104214 · Cited by 4
Choudhary, A., Aouad, M., Saboo, K., Hwang, A., Kechter, J. A., Bordeaux, B., Bhullar, P. K., DiCaudo, D. J., Nelson, S., Comfere, N. I., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104262 · Cited by 1
“Existing studies (Alfasly et al. (2024), Lu et al. (2024)) have shown that using a larger patch-level context can capture semantic tissue cues better and improve performance on morphology-driven tasks.”
InfluentialTraining-free Design of Deep Networks as Ensembles of Clinical ExpertsPreprint
Wu, T., Wu, J., Zhang, Z., & Chen, W. (2024) · medRxiv · doi:10.1101/2024.03.17.24304438 · Cited by 1
Lu, C. Y., Xu, H., Wang, M., Shi, D., Qin, H., Madabhushi, A., Gao, P., & Cong, F. (2024) · Research Square · doi:10.21203/rs.3.rs-4704418/v1 · Cited by 1
Contrastive Learning for Omics-guided Whole-slide Visual Embedding RepresentationPreprint
Yu, S., Kim, Y., Kim, H., Lee, S., & Kim, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.12.632280 · Cited by 2
Bitto, V., Jiang, X., Baumann, M. A., Kather, J. N., & Kurth, I. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.31.646351 · Cited by 1
Thiesen, A. H., Domanskyi, S., pour, A. F., Zhang, J., Sheridan, T., Neuhauser, S. B., Stetson, A. E., Dannheim, K., Cameron, D. B., Ahn, S. S., et al. (2025) · medRxiv · doi:10.1101/2025.06.10.25328700 · Cited by 1
“0.958, respectively), outperforming CTransPath (p 5x10 -4 ) and InceptionV3 (p 5x10 -5 ), with no significant difference between UNI and CONCH themselves Supplementary Figure S4 .”
InfluentialDesigning smart spatial omics experiments with S2OmicsPreprint
Yuan, M., Jin, K., Yan, H., Schroeder, A. R., Luo, C., Yao, S., Domoulin, B., Levinsohn, J. L., Luo, T., Clemenceau, J. R. V., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.21.677634 · Cited by 1
Evaluating Foundation Models with Pathological Concept Learning for Kidney CancerPreprint
Gao, S., Wang, S., Gao, Y., Wang, B., Zhuang, X., Warren, A. Y., Stewart, G. D., Jones, J. O., & Crispin‐Ortuzar, M. (2025) · medRxiv · doi:10.1101/2025.09.29.25336908 · Cited by 1
“Foundation models have significantly enhanced the ability of computational pathology frameworks to make clinically relevant predictions [5, 7, 6].”
Systematic mapping of human tissue microanatomy reveals age-associated remodeling and resiliencePreprint
Buljan, I., Bagó-Horváth, Z., & Rendeiro, A. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.13.682110 · Cited by 1
“Recent advancements in computational pathology and deep learning 19,20 have revolutionized the analysis of histopathological images, enabling a quantitative understanding of tissue structure on a scale that was not possible before.”
REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye diseasePreprint
Shilova, L., Sens, D., Aliyeva, A., Chaudhary, S., Xu, Q., Salin, E., Schiefelbein, J. B., Asani, B., Amarie, O. V., Schneltzer, E., et al. (2025) · medRxiv · doi:10.1101/2025.11.19.25340555 · Cited by 2
“By linking high-dimensional image features to genetic architecture and back to interpretable anatomy, REECAP advances the resolution, interpretability, and translational potential of imaging-genetic studies and offers a blueprint for foundation model alignment in diverse biomedical domains.”
Single-cell spatial multi-omics molecular pathology enabled by SuperFocusPreprint
Lu, Y., Tian, X., Vicari, M., Enninful, A., Bao, S., Bai, Z., Liu, C., Zhang, X., Andrén, P. E., Lundeberg, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.26.696575 · Cited by 1
Uncover This Tech Term: Large Vision-Language Models in Radiology
Faghani, S., Park, Y. W., & Js, P. (2026) · Korean Journal of Radiology · doi:10.3348/kjr.2025.1813 · Cited by 1
“This capability allows retrieval of relevant text from image queries and vice versa, with direct clinical utility [7,8].”
H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics ProfilingPreprint
Gu, Y., Wu, Z., Yan, R., Wang, Z., Li, Y., Lin, S., Cui, Y., Lai, H., Luo, X., Zhou, S. K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.21.717342 · Cited by 2
Jung, K. J., Qiu, J., Cho, S., McDonough, E., Chadwick, C. M., Ghose, S., West, R. B., Brooks, J. D., Ginty, F., Machiraju, R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.15.725535 · Cited by 1
SlideFlame: A data-efficient vision-language model for anatomically grounded pathology reportingPreprint
Kather, J. N., Sainath, S., Lenz, T., Reitsam, N. G., Foersch, S., Hewitt, K., Wölflein, G., Meneghetti, A. R., Liang, J., Wolf, F., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9625986/v1 · Cited by 1
Wang, G., Li, Q., Sang, J., Xiao, Y., Zhang, P., Chen, W., Tran, T., Li, Z., Li, S., & Zhang, T. (2026) · Research Square · doi:10.21203/rs.3.rs-9949149/v1 · Cited by 1
Prospector Heads: Generalized Feature Attribution for Large Models & DataPreprint
Machiraju, G., Derry, A., Desai, A., Guha, N., Karimi, A.-H., Zou, J., Altman, R. B., Ré, C., & Mallick, P. (2024) · PubMed · doi:10.48550/arxiv.2402.11729
Are Stronger Feature Representations All You Need for Histology Image Search?
Chen, R. J., & Mahmood, F. (2024) · NEJM AI · doi:10.1056/aie2400314
Self-citationMehrabian, H., Brodbeck, J., Lyu, P., Vaquero, E., Aggarwal, A., & Diehl, L. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-69244-3 · Cited by 2
Navigating the Future of Healthcare
Musunuri, R. L., & Bhatt, A. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-8552-4.ch003
Hieber, D., Holl, F., Nickl, V., Liesche‐Starnecker, F., & Schobel, J. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1007366
Multi-stain modelling of histopathology slides for breast cancer prognosis predictionPreprint
Sharma, A., Gustafsson, F., Hartman, J., & Rantalainen, M. (2024) · medRxiv · doi:10.1101/2024.11.10.24317066 · Cited by 1
“We used the image encoder from CONCH, which is a vision-language foundation model trained across 1.1 million pathology image-caption pairs [29].”
InfluentialCarretero, I., Meseguer, P., del Amor, R., & Naranjo, V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77731-8_36
A Pathological Diagnosis Method Combining Image-Text Large Model and Rule-Based Reasoning
Lan, H., Sun, Y. L., Hu, P., Yang, R., He, W. P., & Cai, H. (2024) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe62490.2024.00024
Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation
Zhang, Q., Li, J., Liao, P., Hu, J., Guan, T., Han, A., & He, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822745
“Moreover, the advent of pre-trained histopathology vision models has significantly improved the performance of downstream tasks [15]–[17].”
InfluentialReport-Guided Cross-Modal Representation Learning for Predicting EGFR Mutations by Whole Slide Image
Qiao, Q., Shi, J., Jiang, Z., Wang, W., Wu, H., & Zheng, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822285
SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image
Lin, J., Han, G., Zhou, J., Shi, Z., Liu, Z., & Han, C. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821867
Peng, 费. F., Wentian, 思. S., & Minchao, 张. Z. (2024) · Acta Optica Sinica (Online) · doi:10.3788/aosol240448
A generalist model for enhancing brain MRIs
Balbastre, Y., & Fischl, B. R. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01320-5
Dacic, S., Abel, J., Hennek, S. R., Travis, W. D., & Wistuba, I. I. (2025) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2024.11.024
Replicability of large-scale studies in computational pathology
Chamaria, D., Han, W., McClintock, D. S., & Tavolara, T. E. (2025) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3047510
“In no particular order, these include PRISM [5], Virchow [6], CONCH [7], UNI [8], and Prov-GigaPath [9].”
Sridharan, A., DeSilvio, T., Flannery, B. T., Hariri, M., Macbeth, R., Parker, B. J., Elumalai, A., Devi, J., Lovato, A., Maneiro, C., et al. (2025) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.3048987
“The recent development of large foundation models (such as the Segment Anything Model 11 or Captions for Histopathology (CONCH) 12 ) has suggested the potential for more generalized frameworks, which could yield acceptable segmentation accuracy for a wide array of structures on MRI with minimal…”
AGP-Net: A Universal Network for Gene Expression Prediction of Spatial TranscriptomicsPreprint
Yang, Y. Z., Li, X., Pan, L., Zhang, G., Liu, L., & Stone, E. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.09.642276
Zhang, K., Gai, T., Gilley, P., Abdoli, N., Sadri, Y., Thai, T., Fung, K.-M. A., Chen, Y., Moore, K. N., Mannel, R. S., et al. (2025) · Biophotonics and Immune Responses · doi:10.1117/12.3045575
“Meanwhile, the pathologic image features are extracted by a foundation model (CONCH, Contrastive learning from captions for histopathology 12 ).”
Optimization of transfer learning of foundation models for hyperspectral histologic imaging
Hellman, M. D., Ma, L., Yu, J. J. Q., & Fei, B. (2025) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3048965
“Several such foundation models have been recently introduced within the field of histopathology and have achieved state-of-the-art performance on numerous downstream tasks 5-7 .”
Fermin, J. L., Border, S. P., Naglah, A., Shickel, B., Rosa, P. S. L., Tomaszewski, J. E., Jain, S., El‐Achkar, T. M., Eadon, M. T., & Sarder, P. (2025) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3047996
Hou, L., Zhuang, Y., Xie, Y., Kan, H., Huang, Z., & Lin, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5217484
Preclinical Evaluation of an Interactive Image Search System of Oral Pathology
Herdiantoputri, R. R., Komura, D., Ochi, M., Fukawa, Y., Oba, K., Tsuchiya, M., Kikuchi, Y., Matsuyama, Y., Ushiku, T., Ikeda, T., et al. (2025) · Journal of Dental Research · doi:10.1177/00220345251329042 · Cited by 1
“…images, along with advanced vision–language foundation models for histology (Kang et al. 2023; Chen et al. 2024), show promise in enhancing retrieval accuracy and semantic understanding, potentially improving diagnostic precision and efficiency (Ignatov et al. 2024; Lu et al. 2024).”
Domain specific models outperform large vision language models on cytomorphology tasksPreprint
Kukuljan, I., Dasdelen, M. F., Schäfer, J., Buck, M. C., Götze, K. S. S., & Marr, C. (2025) · medRxiv · doi:10.1101/2025.05.05.25326989
Liu, A., & Zeng, P. (2025) · International Conference on Electronic Information Engineering and Data Processing (EIEDP) · doi:10.1117/12.3067471
A Foundation Model for Cell Segmentation from Immunohistochemistry: A Pilot Study
Li, W., Luo, X., Yang, Z., Jia, X., & Li, Z. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980902
“In recent years, the development of foundation models for pathological images[2, 3, 4, 5, 6] has demonstrated powerful performance on label-free datasets.”
Zuo, Y., Wang, L., Liu, J., Feng, S., Wang, Y., Zhu, Q., Shao, W., & Zhang, D. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3575801
“V, VI, and VII, we replace the original feature learner with two feature extractors specialized for medical images, observing slight performance improvements with both CONCH [65] and UNI [66].”
PDA-UNet: A Multiscale Attention UNet for Pathological Image Segmentation
He, G., Zeng, X., Cheng, K., Plain, J., Chen, Z., Yang, H., Liao, C., Liu, Y., & Fang, R. (2024) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv62410.2024.00049
“However, due to the extremely labor-intensive and expensive nature of annotating pathological images, as well as the issues of data acquisi-232 tion and inconsistency in quality, it is challenging to apply general medical image segmentation models to pathological image segmentation [6, 7, 13].”
Applying Self-Supervised Tissue Patch Encoders to Enable Cellular Graph EncodingPreprint
Ding, Z., & Baras, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.10.658651
“…models [10, 11, 55, 53], which derive visual features us-ing contrastive, reconstruction, or hybrid approaches; (2) vision-language models [35, 3], where images are aligned with text data to enhance the contextualization of visual features; and (3) other multi-modal models [1, 29, 28],…”
Faure, G., Soglio, D. B. D., Patey, N., Oligny, L. L., Girard, S., & Séoud, L. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110634
Brahim, I., Zeghlache, R., Frutoso, M., Rochais, M. L., Bandeira, M., Romão, V. C., Asselineau, J., Jaloustre, H., Hillion, S., Saraux, A., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5333602
Cui, J., Gao, Y., Wang, Q., Li, X., Xu, K., Huang, Z.-Y., Zhang, J., & Zuo, C. (2025) · Journal of Computer Science and Technology · doi:10.1007/s11390-025-4342-2
Clinical Stage Prompt Induced Multi-Modal Prognosis
Jin, T., Xie, X., Li, Q., Li, X., & Wang, Y. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3588836
“…the further development of medical large-scale vision-language models, such as PLIP [53], a pathology language–image pretraining model, and CONCH [52], a visual-language foundation model developed using diverse sources of histopathology images–caption pairs, it can be observed that PLIP can even…”
InfluentialWang, C., Pan, C., Meng, C., & Fang, N.-N. (2025) · International Conference on Computing, Networks and Internet of Things (CNIOT) · doi:10.1109/cniot65435.2025.11070476
“Vision-language pretraining (VLP) models, which have shown remarkable success in general domain tasks, offer a promising approach for this integration [2].”
SMatt-DINO: Spatially Aware Masked Attention Network for High Resolution Brain Image Classification
Chaudhuri, M., Das, A., Ram, K., & Sivaprakasam, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_6
Spatia: Multimodal Model for Prediction and Generation of Spatial Cell Phenotypes.Preprint
Kong, Z., Qiu, M., Boesen, J., Lin, X., Yun, S., Chen, T., Kellis, M., & Žitnik, M. (2025) · PubMed
Predicting Multiomics Histopathology Features with Surface Parameterizations
Huang, K., Tan, Z.-H., Yu, W., Wang, X., Ding, Y., Shi-hui, X., Chen, Z., Zhang, Y., Liu, Y., Lin, W., et al. (2025) · Engineering · doi:10.1016/j.eng.2025.07.026
Molecularly informed analysis of histopathology images using natural languagePreprint
Schaefer, M., Nonchev, K., Awasthi, A., Burton, J., Koelzer, V. H., Rätsch, G., & Bock, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.14.664402
Yang, B., Lee, J., Tsai, T. K., Sattler, A. L., Shah, S., Smith, M. A., & Lin, S. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09716-y
Interactive multiple instance learning network for whole slide image analysis
Lai, Q., Vong, C., Yan, T., & Liang, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129338
He, D., Jin, L., Mei, X., Jia, M., Wang, Z., Ren, B., & Cao, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5401357
Pulido-Arias, D., Henderson, R. R., Patel, M., Millien, C., Lomil, J., Jose, M. D., Flambert, G., Bontemps, J., Georges, E., Gunturi, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.27.672012
“For both the TCGA and ZL datasets, quality control and tissue segmentation were performed as per prior literature 19,20 .”
Noman, S. M., Henedak, M. T., & Elattar, M. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae0557
Reference-informed spatial domain detection using weak supervision for spatial transcriptomicsPreprint
Ma, X., Jin, W., Lü, Q., Sun, R. C., & Chen, L. (2026) · Genome Research · doi:10.1101/gr.281260.125
DGHFA: Dynamic Gradient and Hierarchical Feature Alignment for Robust Distillation of Medical VLMs
Xiao, B., Wu, J., Zhong, L., Zou, X., Wu, Y., Wang, G., & Zhang, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_14
Sahai, S., Ramos-Guerra, A. D., Almagro-Pérez, C., Jaume, G., Zhang, A., Rennke, H. G., Weins, A., Ortuño, J. E., Ledesma‐Carbayo, M. J., & Mahmood, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_9
Self-citationTrustworthy Few-Shot Transfer of Medical VLMs Through Split Conformal Prediction
Silva-Rodríguez, J., Ayed, I. B., & Dolz, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_62
Koga, S., Guda, A., Wang, Y., Sahni, A., Wu, J. Z., Rosen, A., Nield, J., Nandish, N., Patel, K., Goldman, H. M., et al. (2025) · medRxiv · doi:10.1101/2025.09.16.25335833
Chen, J., Liu, P., Chen, C., Su, Y., Wang, J., Chen, C., Ai, X., & Lv, X. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00744-0
FUSED: Cross-Domain Integration of Foundation Models for Cancer Drug Response PredictionPreprint
Rössner, T., Balke, J., & Tang, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.30.679434
“In the biomedical domain, pre-trained FMs for medical images [1, 2], drug molecules [3, 4], single-cells [5, 6] and many other modalities are increasingly applied and systematically explored.”
Borlaza, M., & Ibañez, Z. (2025) · Journal of interdisciplinary perspectives · doi:10.69569/jip.2025.620
Detection-driven two-stage framework for intraoperative ROSE WSI classification
Deng, Y., Zhang, Q., Zhou, C., Gao, L., Qin, X., Lu, H., Wang, J., Sun, L., Wang, Y., Zou, D., et al. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109084
Jun, T. J. (2025) · Korean Journal of Medicine · doi:10.3904/kjm.2025.100.5.219 · Cited by 1
Computer Vision Methods for Spatial Transcriptomics: A SurveyPreprint
Zhu, J., Deng, R., Guo, J., Yao, T., Lu, S., Qu, C., Xiong, J., Zhu, Y., Lu, Z., Yang, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.13.682148
“In fact, advanced paradigms from computer vision and natural language processing are increasingly being adapted to ST research [17], [34], [57].”
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
