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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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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]
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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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InfluentialSelf-citationKnowledge-enhanced pretraining for vision-language pathology foundation model on cancer diagnosis
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“Given already very powerful image representations [6, 18, 26, 27, 36], it is difficult to This CVPR paper is the Open Access version, provided by the Computer Vision Foundation.”
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Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology
Hoang, D.-T., Shulman, E. D., Dhruba, S. R., Nair, N. U., Barman, R. K., Cantore, T., Biswas, S., Lalchungnunga, H., Singh, O., Chung, Y., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-25-4350 · Cited by 14
Planchard, D., Cozic, N., Mosele, M. F., Corcos, N., Bescond, L. L., Pradat, Y., Ghigna, M., Nachabeh, G., Alfaro, A., Catelain, C., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.03.017 · Cited by 11
Wang, R., Gunesli, G. N., Skingen, V. E., Valen, K.-A. F., Lyng, H., Young, L. S., & Rajpoot, N. (2025) · npj Precision Oncology · doi:10.1038/s41698-024-00778-5 · Cited by 11
Wu, X., Li, Y., Chen, J., Chen, J., Zhang, W., Lu, X., Zhong, X., Zhu, M., Yi, Y., & Bu, H. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-01968-0 · Cited by 12
“…SSL algorithm based on MoCo v3) [9], CONCH (a vision-language foundation model pretrained on diverse tissue pathology images and biomedical texts) [10], REMEDIS (combining large-scale supervised transfer learning with natural images and intermediate contrastive self-supervised learning for…”
How artificial intelligence is transforming pathology
Kwon, D. (2025) · Nature · doi:10.1038/d41586-025-01576-0 · Cited by 14
Towards a cardiovascular magnetic resonance foundation model for multi-task cardiac image analysis
Jacob, A. J., Borgohain, I., Chițiboi, T., Sharma, P., Comaniciu, D., & Rueckert, D. (2025) · Journal of Cardiovascular Magnetic Resonance · doi:10.1016/j.jocmr.2025.101967 · Cited by 19
“The inversion time was individually adjusted to null normal myocardium.”
Understanding pre-training data effects in retinal foundation models using two large fundus cohorts
Zhou, Y., Wang, Z., Wu, Y., Ong, A. Y., Wagner, S. K., Ruffell, E., Chia, M. A., Guan, Z., Ju, L., Engelmann, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-70077-z · Cited by 17
Panacea: A foundation model for clinical trial search, summarization, design, and recruitmentPreprint
Lin, J., Xu, H., Wang, Z., Wang, S., & Sun, J. (2024) · medRxiv · doi:10.1101/2024.06.26.24309548 · Cited by 15
Jesinghaus, M., Schmitt, M. P., Foersch, S., & Konukiewitz, B. (2025) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-025-04241-z · Cited by 10
Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor
Zheng, K., Duan, J., Wang, R., Chen, H., He, H., Zheng, X., Zhao, Z., Jing, B., Zhang, Y., Liu, S., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101785 · Cited by 13
Wang, C., Wan, Y., Li, S., Qu, K., Zhou, X., He, J., Ke, J., Yu, Y., Wang, T., & Shen, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3501352 · Cited by 13
“Recent advancements in whole slide image (WSI) processing have further enhanced the applicability of FMs, with models like CONCH [28] and Virchow [29] demonstrating state-of-the-art performance in large-scale histopathological image analysis.”
Iacucci, M., Santacroce, G., Meseguer, P., Diéguez, A., del Amor, R., Kolawole, B. B., Chaudhari, U., Zammarchi, I., Hayes, B. D., Crotty, R. K., et al. (2025) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjaf108 · Cited by 10
“16 Modality-specific foundation models were considered for feature extraction due to their ability to learn rich and generalizable data representations that enhance performance in downstream tasks.”
Ahn, J., Kang, B. G., Chang, M. Y., & Yoon, S. (2025) · Clinical and Experimental Otorhinolaryngology · doi:10.21053/ceo.2025-00121 · Cited by 10
“Examples include analyzing rare diseases [95], leveraging EHRs for clinical predictions [96], and performing visual question answering in computational pathology [97].”
Artificial intelligence in prostate cancer
Li, W., Hu, R., Zhang, Q., Yu, Z., Deng, L., Zhu, X., Xia, Y., Song, Z., Cimadamore, A., Chen, F., et al. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003689 · Cited by 10
“Lu et al [40] 2024 1,170,000 image–caption pairs”
Self-citationOmar, M., Fanelli, G. N., Socciarelli, F., Ullanat, V., Puchala, S. R., Wen, J., Chowdhury, A., Valencia, I., Scatena, C., Marchionni, L., et al. (2025) · Laboratory Investigation · doi:10.1016/j.labinv.2025.104220 · Cited by 13
Recent Advances in Medical Image Classification
Dao, L., & Ly, N. Q. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150727 · Cited by 14
“ Med-VLM: GLoRIA [48], ASG [49], MeDSLIP [50], SAT [51], CONCH [52], and ECAMP [53].”
MethodMoxley-Wyles, B., & Colling, R. T. (2024) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2024.08.001 · Cited by 9
Re-identification from histopathology images
Ganz, J., Ammeling, J., Jabari, S., Breininger, K., & Aubreville, M. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103335 · Cited by 14
Shi, J., Li, C., Gong, T., Wang, C., & Fu, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3485120 · Cited by 10
“6) Effect of VLMs: To explore the effect of various VLMs on the model’s performance, we conducted experiments with pathology-specific VLMs ( i.e. , MI-Zero [30], PLIP [31], and CONCH [40].”
Bhattacharya, I., Stacke, K., Chan, E., Lee, J. H., Tse, J. R., Liang, T., Brooks, J. D., Sonn, G. A., & Rusu, M. (2024) · Medical Physics · doi:10.1002/mp.17476 · Cited by 9
“…vision transformer-based foundation model DinoV2 39 as the CT feature extractor (circle-1),and the pretrained vision-language foundation model CONCH 40 to select cancer patches from the WSI,generating a pathology cancer Region-of-Interest (circle-2),as well as to extract pathology features…”
FM-APP: Foundation Model for Any Phenotype Prediction via fMRI to sMRI Knowledge Transfer
He, Z., Li, W., Liu, Y., Liu, X., Han, J., Zhang, T., & Yuan, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3506734 · Cited by 12
“The semantic features generated by BiomedCLIP, a large pre-trained language model fine-tuned on a comprehensive medical corpus [33], are deemed sufficient to capture the rich semantics and nuances of phenotype descriptions.”
Enhanced RGBT Tracking Network With Semantic Generation and Historical Context
Gao, Z., Zhou, D., Cao, J., Liu, Y., & Shan, Q. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551143 · Cited by 9
“Recognizing the impressive capabilities of large language models (LLMs) in multimodal downstream tasks [7], [8], [9], along with the potential benefits they offer for tracking—such as enriched semantic information, improved target localization, and enhanced interpretability— we integrate language…”
Ullah, E., Baig, M. M., Waqas, A., Rasool, G., Singh, R. P., Shandilya, A., GholamHossieni, H., & Parwani, A. V. (2025) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000498 · Cited by 9
Self-citationDing, R.-W., Luong, K.-D., Rodriguez, E., da Silva, A. C. A. L., & Hsu, W. (2024) · Scientific Reports · doi:10.1038/s41598-025-99042-4 · Cited by 13
“4) CONCH: 512 deep features were extracted from CONCH, a vision language pathology foundation model pre-trained on 1.17M image caption pairs [42].”
MSMMIL: Multi-scan Mamba-based Multiple Instance Learning for whole slide image classification
Zhong, H., Ding, M., Zhao, C., Zhang, Y., Wang, T., & Lei, B. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113871 · Cited by 10
Integrating images and genomics for multi-modal cancer survival analysis via mixture of experts
Zhang, W., Xu, W., Chen, T., Sakal, C., & Li, X. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103521 · Cited by 12
ZoomLDM: Latent Diffusion Model for multi-scale image generation
Yellapragada, S., Graikos, A., Triaridis, K., Prasanna, P., Gupta, R., Saltz, J., & Samaras, D. (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/cvpr52734.2025.02184 · Cited by 14
“For better comparisons, we also compute LPIPS [49] and CONCH [28] similarity, which downsamples the image to 224 × 224 as well as UNI similarity, which we consider on a per 256 × 256 patch-level.”
MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology
Nielsen, A. W., Manoochehri, H. E., Zhong, H., Panwar, V., Jarmale, V. N., Jasti, J., Nourani, M., Rakheja, D., Brugarolas, J., Kapur, P., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01504-x · Cited by 9
“From several models evaluated (UNI [35], Virchow [36], GigaPath [37], CONCH [38], RetCCL [39]), UNI (v1) was selected due to its strong performance on the pretext task and its computational manageability (Additional file 1: Fig.”
Frascarelli, C., Venetis, K., Marra, A., Concardi, A., D’Ercole, M., Mangione, E., Negrelli, M., Porta, F. M., Keswani, S., Curigliano, G., et al. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00857-1 · Cited by 12
Foundation Models Meet Medical Image Interpretation
Jiao, L., Hao, J., Li, R., Li, L., Liu, X., Liu, F., Ma, W., Chen, P., Huang, Z., Yang, J., et al. (2026) · Research · doi:10.34133/research.1024 · Cited by 12
Al-Jebrni, A. H., Jiang, Z., Qin, Y., Qiu, Y., Jiang, N., Li, G., Zheng, Y., Wong, T. Y., Kim, S., Jung, Y., et al. (2026) · Computational Visual Media · doi:10.26599/cvm.2026.9450530 · Cited by 9
Hamamoto, R., Koyama, T., Takahashi, S., Yasuda, T., Kobayashi, K., Akagi, Y., Kouno, N., Sudo, K., Hirata, M., Sunami, K., et al. (2026) · Journal of Hematology & Oncology · doi:10.1186/s13045-026-01781-y · Cited by 8
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments
Gao, G., Song, A. H., Wang, F., Brenes, D., Wang, R., Chow, S. S. L., Bishop, K. W., True, L. D., Mahmood, F., & Liu, J. T. C. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00689 · Cited by 7
“…approach, as opposed to a full 3D approach, is that it is able to evaluate 3D pathology data at high resolution along the depth axis, and also takes advantage of the emerging family of 2D feature extraction models for 2D pathology images [12, 25, 36, 54], which are lacking in 3D feature extraction.”
BackgroundMethodSelf-citationUsing artificial intelligence to prioritize pathology samples: report of a test drive
Rienda, I., Vale, J., Pinto, J. C., Polónia, A., & Eloy, C. (2024) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-024-03988-1 · Cited by 8
“Different efforts have been made recently to bring foundation model-based tools to pathology, with a successful multi-task performance [5, 6].”
Shi, J., Sun, D., Jiang, Z., Du, J., Wang, W., Zheng, Y., & Wu, H. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102502 · Cited by 8
Zhang, H., Li, Z., Chen, H., Liu, M., Qu, Z., & Zhou, S. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.112687 · Cited by 8
Du, R. F., Carbonell, E. L., Huang, J., Liu, S., Wang, X., Shen, D. G., & Ke, J. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3551913 · Cited by 10
Chen, Y., Yang, M., & Hua, Q. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107734 · Cited by 8
Augmented reality microscopy to bridge trust between AI and pathologists
Badve, S., Kumar, G. L., Lang, T., Peigin, E., Pratt, J., Anders, R. A., Chatterjee, D., González, R. S., Graham, R. P., Krasinskas, A. M., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00899-5 · Cited by 9
“Following the general research trend of large language models, AI foundation models are currently dominating the computational pathology research space 12,15,43 – 47 .”
Albahri, M., Sauter, D., Nensa, F., Lodde, G. C., Livingstone, E., Schadendorf, D., & Kukuk, M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.017 · Cited by 9
“5 [18], Virchow [11], and CTransPath [19]) — each paired with its recommended slide encoder — and evaluated all combinations under identical 5-fold cross-validation and external testing protocols [20] using three downstream classifiers (XGBoost [14], Random Forest [21], and Logistic Regression…”
Current and future applications of artificial intelligence in lung cancer and mesothelioma
Roche, J. J., Seyedshahi, F., Rakovic, K., Thu, A. W., Le Quesne, J., & Blyth, K. G. (2025) · Thorax · doi:10.1136/thorax-2024-222054 · Cited by 11
“9 10 Supervised learning has generally been used to train models that assist pathologists with defined clinical tasks, resulting in improved efficiency, accuracy or consistency.”
Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images
Rahaman, M. M., Millar, E. K. A., & Meijering, E. H. W. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3584802 · Cited by 8
“Thesepromptsareencodedusingapretrainedtext encoder g (from the CONCH model [28]) to obtain robust class embeddings { t 1 , t 2 , . . . , t C } , where C is the number of classes.”
InfluentialMulti-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Albastaki, S., Sohail, A., Ganapathi, I. I., Alawode, B., Khan, A. A., Javed, S., Werghi, N., Bennamoun, M., & Mahmood, A. (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/cvpr52734.2025.02413 · Cited by 8
Specialized curricula for training vision language models in retinal image analysis
Holland, R., Taylor, T. R. P., Holmes, C., Riedl, S., Mai, J., Patsiamanidi, M., Mitsopoulou, D., Hager, P., Müller, P., Paetzold, J. C., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01893-8 · Cited by 12
Multimodal AI-based risk stratification for distant metastasis in nasopharyngeal carcinoma
Zhou, J., Wibawa, M. S., Wang, R., Deng, Y., Huang, H., Luo, Z.-X., Xia, Y., Guo, X., Young, L. S., Lo, K. W., et al. (2025) · ESMO Open · doi:10.1016/j.esmoop.2025.105809 · Cited by 9
Artificial intelligence-assisted prostate cancer diagnosis for reduced use of immunohistochemistry
Blilie, A., Mulliqi, N., Ji, X., Szolnoky, K., Boman, S. E., Titus, M., Gonzalez, G. M., Asenjo, J., Gambacorta, M., Libretti, P., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01185-y · Cited by 10
“Recently, pathology foundation models (FM) have shown promise in pan-cancer detection 23 – 26 .”
Imran, M., & Lee, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3656368 · Cited by 11
“Domain-focused systems (e.g., CONCH, EchoFont) demonstrate promising task-specific gains [23], [24], yet generalization across institutions and settings remains limited.”
InfluentialLópez, P. D. D., Cárdenas‐Montes, M., Troya, J., Ocaña-Tienda, B., Cepeda, S., Martínez, R. M., & Corrales-García, E. M. (2025) · Clinical & Translational Oncology · doi:10.1007/s12094-025-03948-4 · Cited by 8
“…models trained on extensive datasets comprising histopathology images and biomedical literature, enhance AI’s ability to interpret pathology slides and identify disease-specific features, significantly enhancing diagnostic precision and facilitating more nuanced tumor classification [29, 64].”
Lee, Y., Liu, X., Hao, M., Liu, T., & Regev, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.10.627865 · Cited by 8
Precision oncology in patients with breast cancer: towards a ‘screen and characterize’ approach
Rassy, E., Mosele, M. F., Meglio, A. D., Pistilli, B., & André, F. (2024) · ESMO Open · doi:10.1016/j.esmoop.2024.103716 · Cited by 7
Fedoriw, Y. D., Silva, O., Znaor, A., & Macintyre, E. A. (2024) · Histopathology · doi:10.1111/his.15340 · Cited by 7
“42 While not attempted on lymphoma classification yet, CONCH has been able to achieve high accuracy ( > 90”
Rahman, T., Baras, A. S., & Chellappa, R. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100636 · Cited by 7
Generalized domain prompt learning for accessible scientific vision-language models
Cao, Q., Chen, Y., Lu, L., Sun, H., Zeng, Z., Yang, X., & Zhang, D. (2025) · Nexus · doi:10.1016/j.ynexs.2025.100069 · Cited by 7
Lin, R., Chen, Y., Li, Y., Tan, Y., Wang, C., Wang, Z., Sun, M., Wang, L., Wu, Y., Ou, Q., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002922 · Cited by 9
“Another study introduced a visual-lan-guage foundation model for computational pathology, achieving state-of-the-art performance in tasks like image classification and segmentation [ 12 ] .”
Artificial Intelligence in Pathology: Advancing Large Models for Scalable Applications
Xiao, Z., Feng, B., Yang, J., Sun, G., Shen, Y., Xu, S., Yang, L., Xu, H., Zhang, M., & Wang, S. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095814 · Cited by 9
The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?
El‐Khoury, R., & Zaatari, G. S. (2025) · Diagnostics · doi:10.3390/diagnostics15182308 · Cited by 10
“…models are being developed and include vision encoders such as UNI [72], CTransPath [73], and Virchow [74], vision–language encoders such as PLIP [71], Prov-GigaPath [75], and CONCH [76], and, as we write this review, THREAD, a molecular-driven foundation model for oncologic pathology [77].”
Self-supervised learning for data augmentation in histopathology image segmentation
Almubarak, H. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-23280-9 · Cited by 8
Bilal, M., Gulzar, M. A., Jaffar, N., Alabduljabbar, A., Altherwy, Y. N., Alsuhaibani, A., & Almarshad, F. A. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102680 · Cited by 11
Artificial intelligence in genitourinary pathology
Patel, A., Parwani, A. V., & Satturwar, S. P. (2025) · Histopathology · doi:10.1111/his.70020 · Cited by 10
Self-citation
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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