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 301–400 of 968
Domain Adaptation and Generalization Using Foundation Models in Healthcare ImagingPreprint
Ying, H., Lia, Y., & Fu, Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5345726 · Cited by 4
Liu, Y., Zhang, L., Gu, M., Xiao, Y., Yu, T., Tao, X., Zhang, Q., Wang, Y., Shen, D. G., & Li, Q. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102482 · Cited by 3
Bahadir, C. D., Akar, G. B., & Sabuncu, M. R. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00042 · Cited by 3
“Originally developed for natural images and trained on an extensive dataset of approximately 400 million samples [26], this approach has been subsequently adapted to the medical domain [32, 34], with a specific focus on the sub-domain of digital pathology [14,15,20,21,23,28].”
InfluentialScaling Chest X-Ray Foundation Models From Mixed Supervisions for Dense Prediction
Wang, F., & Yu, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3589928 · Cited by 5
“Prior work has shown that domain-specific unimodal pretraining significantly improves performance in medical multimodal tasks [9], [56], [57].”
Decrypting cancer's spatial code: from single cells to tissue niches
Celik, C., Shi, P., Withnell, E., Lam, H. W., & Secrier, M. (2025) · Molecular Oncology · doi:10.1002/1878-0261.70100 · Cited by 4
“Histopathology foundation models, including Phikon-v2 [139], UNI [136] and CONCH [121], originally developed as powerful feature extractors from whole slide images, are increasingly being adapted to predict treatment outcomes and gene expression patterns directly from H&E-stained sections [133,140].”
Wang, C., Muzakky, H., Chung, Y.-P., Lai, P.-J., & Chao, T. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103742 · Cited by 3
Wang, Y., Chang, M.-H., Tsai, H.-H., Chien, C.-J., & Wang, J.-C. (2025) · Diagnostics · doi:10.3390/diagnostics15172131 · Cited by 3
Weakly Supervised Panoptic Segmentation for Defect-Based Grading of Fresh Produce
Knott, M., Odion, D., Sontakke, S., Karwa, A., & Defraeye, T. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00543 · Cited by 3
“Other foundation models are designed to excel in a certain task [15, 27] or domain problem [11, 19, 22] within a modality.”
Few-Shot, Now for Real: Medical VLMs Adaptation Without Balanced Sets or Validation
Silva-Rodríguez, J., Shakeri, F., Bahig, H., Dolz, J., & Ayed, I. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_23 · Cited by 3
Regularized Low-Rank Adaptation for Few-Shot Organ Segmentation
Baklouti, G., Silva-Rodríguez, J., Dolz, J., Bahig, H., & Ayed, I. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_49 · Cited by 3
Meng, X., Wong, W., Pushpanathan, K., Srinivasan, S., Xue, C., Wang, M., Miao, H., Li, H., Yang, L., Cen, L., et al. (2025) · Eye · doi:10.1038/s41433-025-04013-8 · Cited by 4
Fiala, G., Plass, M., Harb, R., Regitnig, P., Skok, K., Zoughbi, W. A., Zerner, C., Torke, P. R., Kargl, M., Müller, H., et al. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103368 · Cited by 3
A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer
Zhao, Z., Chen, D., Wei, X., Li, S., Zhang, X., Lin, W., Zheng, X., Zheng, K., Wu, E., Wen, X., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102479 · Cited by 3
“54 Specifically, cell type descriptions were generated by inserting cell type text into predefined templates, after which these descriptions were encoded using CONCH’s text encoder (Table S9).”
Fu, M., Liu, L., Fu, F., Ouyang, J., Shi, X., Duan, S., Guan, T., He, Y., Cheng, Z., & Zhu, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-19195-0 · Cited by 4
“Addressing task diversity, the visual-language foundation model Conch utilized CoCa 22 , pre-training on pathology images from multiple sources alongside biomedical text, incorporating image and text encoders with a multimodal fusion decoder 23 .”
Foundation models: Insights and implications for gastrointestinal cancer
Shi, L., Huang, R., Zhao, L.-L., & Guo, A.-J. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i47.112921 · Cited by 3
He, Q., Tan, H., Xiao, B., Tan, Y., Peng, X., Peng, C., Yue, X., Jiang, L., Cao, Y., Lv, F. J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02232-7 · Cited by 3
An Introduction to Pathology Foundation Models
Veremis, B. M., Chen, S., & Campanella, G. S. V. (2026) · Head and Neck Pathology · doi:10.1007/s12105-025-01878-9 · Cited by 3
“Another application of this concept, CONCH, was developed by using CoCa (COntrastive CAp-tioner [35]) as the pretraining method, allowing it to combine image and text embeddings before decoding both into text that describes the image [36].”
Agrawal, A., & Thomann, S. (2026) · American Journal Of Pathology · doi:10.1016/j.ajpath.2026.01.007 · Cited by 4
WSISum: WSI summarization via dual-level semantic reconstruction
Wang, B., Zhang, K., Wang, Y., Gu, Y., Luan, H., Zhou, Y., Hu, T., Wang, R., Yang, Z., Jiang, Z., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103970 · Cited by 3
Gindra, R. H., Palla, G., Nguyen, M., Wagner, S., Tran, M., Theis, F. J., Saur, D., Crawford, L. A., & Peng, T. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00128 · Cited by 13
“Recent models have proposed to align pathology images with text on patch-level (PLIP [17], QUILT-1M [19], CONCH [29], MUSK [55]) or on slide-level (PRISM [44], GigaPath [57], TITAN [12]).”
InfluentialTime and person sensitive foundation model for disease prediction and risk stratification
Wang, Z., Zhou, Y., Wu, Y., Goh, J. H. L., Zou, K., Guan, Z., Chen, Y., Yang, G. D., Zhang, P., Yin, C., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02524-6 · Cited by 3
Vision–language foundation model driven agentic AI systems for healthcare
Chen, L., Xu, X., Qin, Y., Li, H., & Jiang, N. (2026) · The Visual Computer · doi:10.1007/s00371-026-04437-7 · Cited by 3
Learning with less supervision: A survey of label-efficient learning for medical image analysis
Jin, C., Guo, Z., Lin, Y., Luo, L., & Chen, H. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104062 · Cited by 49
Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping
He, D., Zhou, X., Guan, W., Zhang, L., Zhang, X., Xu, S., Wang, G., Wang, L., Yuan, X., Ma, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71715-2 · Cited by 7
“Vision-only models such as Virchow 24 show promise for rare cancer detection through fi ne-tuning, while vision-language models like TITAN 25 , CONCH 26 , and KEEP 27 leverage multimodal knowledge for zero-shot classi fi cation.”
Gao, R., Yang, Z., Yuan, X., Wang, Y., Xia, Y., Zhang, Y., Zheng, B., Gong, Y., Yue, Y., Tu, Y., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71558-x · Cited by 6
Lian, S., Cai, J., Pan, D., Chen, G., Xu, H., Zhang, F., Fan, G., Pei, J., & Li, S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02852-w · Cited by 5
“In the field of medical image segmentation, Visual Language Models (VLMs) offer promising solutions by combining techniques from the computer vision communities and the natural language processing communities [25].”
DxDirector: an agentic large language model driving the full-process clinical diagnosis
Xu, S., Huang, X., Wei, Z., Pang, L., Shen, H., & Cheng, X. (2026) · Nature Communications · doi:10.1038/s41467-026-71928-5 · Cited by 7
“For instance, DxDirector-7B could call specialized pathology and imaging analysis models with visual understanding capabilities for Radiology 35 – 38 , Echocardiography 39 , Cell Slice Analysis 40 , Pathology 41 , and related domains.”
Polit, F., Bahmad, H. F., Kassab, M. B., Elajami, M. K., Recine, M. A., Alghamdi, S., & Poppiti, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1833926 · Cited by 3
“…for Histopathology), by Lu et al. (2024), is a vision-language foundation model trained on 1.17 million histopathology image-caption pairs, achieving state-of-the-art results across 14 benchmarks encompassing image classi fi cation, segmentation, cap-tioning, and cross-modal retrieval tasks (97).”
InfluentialAI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment
Paul, S., Isaacs, H. M., & Côté, R. J. (2026) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2026.2665801 · Cited by 3
“…models represent a promising transformative advance in computational pathology, enabling large-scale pre-training on extensive collections of histopathology images that often include tens of thousands to hundreds of thousands of slides spanning diverse tissue types and staining protocols [45–47].”
Pan, J., Jian, B., Hager, P., Zhang, Y., Liu, C., Jungmann, F., Li, H., Canisius, J., You, C., Wu, J., et al. (2026) · Nature Health · doi:10.1038/s44360-026-00152-8 · Cited by 25
End-to-end multimodal pathology foundation model with clinical dialogue
Vorontsov, E., Shaikovski, G., Casson, A., Viret, J., Zimmermann, E., Tenenholtz, N., Wang, Y. K., Bernhard, J., Godrich, R., Retámero, J. A., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04521-4 · Cited by 25
Berloco, F., Zaccaria, G. M., Altini, N., Colucci, S., & Bevilacqua, V. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102526 · Cited by 3
Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach
Amidi, E., Ramzanpour, M., Chen, M., Boucher, T., Varma, M., Samec, T., Lamon, B. D., Stransky, N., Miglarese, M. R., Oberley, M. J., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01037-x · Cited by 4
“Since completing the primary analyses for this study, several newer large-scale feature extractors have been introduced, such as PathCLIP 69 , Virchow 57 , Virchow 2 70 , UNI 59 , and CONCH 71 , which have demonstrated impressive performance across a variety of pathology tasks.”
Jalili, J., Huynh, J., Walker, E., Chuter, B., Bowd, C., Heinke, A., Belghith, A., Goldbaum, M. H., Fazio, M. A., Girkin, C. A., et al. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.11.31 · Cited by 3
Enhancing Image Retrieval Performance With Generative Models in Siamese Networks
Golfe, A., Colomer, A., Padres, J., & Naranjo, V. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3543907 · Cited by 3
Editorial for Special Issue on Foundation Models for Medical Image Analysis
Wang, X., Wang, D., Li, X., Rittscher, J., Metaxas, D., & Zhang, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103389 · Cited by 3
Zhang, X., Wang, T., Yan, C., Najdawi, F., Zhou, K., Ma, Y., Cheung, Y., & Malin, B. (2024) · medRxiv · doi:10.1101/2024.12.27.24319715 · Cited by 3
TLPath predicts telomere length in human tissues from histopathology imagesPreprint
Yadav, A., Alvarez, K., Adeleye, A., Wang, Y. X., & Sinha, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.04.641489 · Cited by 3
“We analyzed the average similarity confidence scores of CONCH for all terms across all patches, grouped by UNI feature activation and telomere length.”
InfluentialBenchmarking pathology foundation models for non-neoplastic pathology in the placentaPreprint
Peng, Z., Ayad, M. A., Jing, Y., Chou, T., Cooper, L., & Goldstein, J. A. (2025) · medRxiv · doi:10.1101/2025.03.19.25324282 · Cited by 6
“8, 15 Of three recent benchmarking papers Our findings with whole-slide lesion diagnosis are telling – with the more complicated model design built atop foundational embeddings, non-pathology models provide sufficiently valuable information.”
InfluentialA fusocelular skin dataset with whole slide images for deep learning models
del Amor, R., López-Pérez, M., Meseguer, P., Morales, S., Terrádez-Mas, L., Aneiros‐Fernández, J., Javier Mateos, A., Molina, R., & Naranjo, V. (2025) · Scientific Data · doi:10.1038/s41597-025-05108-3 · Cited by 3
Doerfler, R., Chen, J., Kim, C., Smith, J. D., Harris, M. K., Singh, K. B., Isett, B. R., Dadey, R., Brown, D. D., Lee, A. V., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.24.660824 · Cited by 3
“However, most existing pathology AI tools were trained on primary human tissues 29-35 , and their application to organoid systems remains largely unexplored.”
A fully annotated pathology slide dataset for early gastric cancer and precancerous lesions
Wang, C., Ge, J., Niu, Y., Ding, C., Fan, Y., Chang, H., Yang, Z., Ran, C., Teng, X., Wang, X., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05679-1 · Cited by 3
A clinically relevant morpho-molecular classification of lung neuroendocrine tumoursPreprint
Sexton‐Oates, A., Mathian, É., Candeli, N., Lim, Y., Voegele, C., Genova, A. D., Mangé, L., Li, Z., van Weert, T. J. J., Hillen, L. M., et al. (2025) · medRxiv · doi:10.1101/2025.07.18.25331556 · Cited by 3
“Volcano plots represent the statistical significance ( q value, y-axis) versus the effect size (x-axis) of the differences between molecular groups in terms of cosine similarity between CONCH WSI tile embeddings and text prompt embeddings, for different prompts (points).”
OpenSlideFM: A Computationally Efficient Multi-Scale Foundation Model for Computational PathologyPreprint
Zafar, S. A., Qin, W., Chengliang, L., Khan, A. A., Nazir, A., Khalid, F., & Faisal, M. S. (2025) · medRxiv · doi:10.64898/2025.12.09.25341769 · Cited by 2
Glettig, M., Ehrensperger, T., Yates, J., & Boeva, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.22.665986 · Cited by 3
Image Analysis: Understanding and Mitigating Batch Effects in Histopathology
Gindra, R. H., Boushehri, S. S., Wagner, S. J., Tran, M., Winter, D. J. E., Schnabel, J. A., Saur, D., Marr, C., & Peng, T. (2025) · Trillium Pathology · doi:10.47184/tp.2025.01.03 · Cited by 2
Multi-instance curriculum learning for histopathology image classification with bias reduction
Mi, Z., Zhang, J., Liu, X., Yue, G., Yue, J., Wei, M., Li, Y., & Wu, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103647 · Cited by 2
Implications of Neural Compression to Scientific Images
Cardenuto, J. P., Krinsky, J., Nogueira, L. S. L., Bharati, A., & Moreira, D. (2025) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3733102.3733148 · Cited by 2
Rethinking Chart Understanding Using Multimodal Large Language Models
Tanasa, A.-M., & Oprea, S. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.065421 · Cited by 4
Sun, Y., Wu, H., Zhu, C., Si, Y., Chen, Q., Zhang, Y., Zhang, K., Li, J., Cai, J., Wang, Y., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3584857 · Cited by 2
“Consequently, several CLIP-based models tailored for pathology have recently emerged, including Quilt-Net [4], PathCLIP [2], PLIP [3], CONCH [28], and PathGen-CLIP [5].”
Bi, D., & Zhang, Y. (2025) · Mathematics · doi:10.3390/math13132178 · Cited by 2
PathFL: Multi-alignment Federated Learning for pathology image segmentation
Zhang, Y., Chen, F., Qi, Y., Yang, G., & Fu, H. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103670 · Cited by 3
Unveiling the future: the impact of artificial intelligence in diagnostic pathology
Verma, K. K., Palmal, R., & Yadav, P. (2025) · Surgical and Experimental Pathology · doi:10.1186/s42047-025-00197-1 · Cited by 5
“Those are called foundation models (Lu et al. 2024; Xu et al. 2024a; Ochi et al. 2025).”
Xu, J., Jiang, M., Koga, S., Zhang, N. R., & Huang, Z. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1746714 · Cited by 3
“We evaluated our model’s ROI rankings against these ground truth measures using three complementary metrics: • Spearman’s rank correlation ( ρ ) : Measures global concordance between model and ground truth rankings, indicating overall prioritization accuracy (Spearman, 2010).”
Pathology Image Compression with Pre-trained Autoencoders
Yellapragada, S., Graikos, A., Triaridis, K., Li, Z., Nandi, T., Madduri, R., Prasanna, P., Saltz, J. H., & Samaras, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04937-7_42 · Cited by 2
Foundation Model for Medical Imaging: A Comprehensive Review
Jiao, L., Yang, J., Li, R., Liu, F., Liu, X., Chen, P., Guo, Y., Li, L., Shang, R., & Ma, W. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3618796 · Cited by 4
Prospects for Artificial Intelligence-Based Pathological Diagnosis of Renal Transplant Biopsy
Iwadoh, K., & Tonsho, M. (2025) · The Nephron journals/Nephron journals · doi:10.1159/000549067 · Cited by 2
Yu, Z., Zhang, S., Qiao, N., Zhao, Y., Yu, L., Peng, T., & Zhang, X. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103840 · Cited by 2
PREMISE: Individual Preference-aware Multi-modal Cooperation for Survival Prediction
Cui, J., Li, Y., Wu, X., Zhou, J., & Wang, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755079 · Cited by 2
“Following [5, 42], the number of time intervals 𝐾 is set to 4.”
Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification
Guo, H., Zhang, Q., Gao, Z., Yang, S., Peng, S., Tao, X., Yu, T., Wang, Y., & Li, Q. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755262 · Cited by 2
“As we utilize image-text modalities as the model input, we further compare our approach with SOTA image-text based methods using UNI as feature extractor, i.e. , MIZero [29], TOP [34], and CONCH [28].”
Su, X. Q., Mao, Q., Wu, Z., Lin, X., You, S., Liao, Y., & Xu, C. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02042-x · Cited by 2
“In evaluations across multiple datasets, MedSAM demonstrated superior versatility compared to task-speci fi c models 10 .”
Clinically aligned multi-modal image-text model for pan-cancer prognosis prediction
Lee, J., Leiby, J. S., Takemaru, L., Huang, Y., Noh, M., Kim, J., Lee, B., Lee, M. E., Oldridge, D. A., Eun, Y., et al. (2025) · BioData Mining · doi:10.1186/s13040-025-00495-0 · Cited by 2
CLIP-Guided Generative network for pathology nuclei image augmentation
Zhang, Y., Liu, Q., Chen, Q., & Bai, X. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103908 · Cited by 2
nnMIL: a generalizable multiple instance learning framework for computational pathology
Luo, X., Xiang, J., Ji, Y., & Li, R. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01767-8 · Cited by 3
Srinivasan, S., Ai, X., Lo, T. W. S., Gilson, A., Zou, M., Zou, K., Kim, H., Yang, M., Pushpanathan, K., Yew, S. M. E., et al. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.101050 · Cited by 4
He, H., Peng, P., Shi-Tong, Y., Wang, M.-G., Zhang, X.-F., & Chen, L. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00938-y · Cited by 3
Lowry, N. C., Breto, A., Wallaengen, V., Algohary, A., Tapia-Stoll, N., Gaston, S. M., Prakash, N. S., Freitas, P. F. S., Kryvenko, O. N., Castillo, P., et al. (2026) · Cancer Imaging · doi:10.1186/s40644-026-00988-z · Cited by 4
“This is typical of foundation models which specialize in multimodal data, requiring the transformation of imaging features into tokens for large-language model outputs, contours for image segmentation, or to generate multiple predictions from a single set of image patches [19–21].”
Zhang, Y., Wang, J., Liang, B., Yang, Y., Wu, S., & Tong, C. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113151 · Cited by 2
Lensless autofocusing based on symmetric diffraction shifts for high-throughput whole-slide imaging
Wu, H., Tang, W., Cai, Y., Cao, T., Huang, X., Wu, J., Zhang, J., Wu, W., Jiang, S., & Guo, C. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.114823 · Cited by 2
Chen, S., Parreno-Centeno, M., Booker, G., Verghese, G. E., Mohamed, F. S., Arslan, S., Pandya, P., Oozeer, A., D’Angelo, M., Barrow‐McGee, R., et al. (2026) · npj Breast Cancer · doi:10.1038/s41523-026-00896-2 · Cited by 2
Multimodal dynamic fusion framework for survival prediction in clear cell renal cell carcinoma
Fu, B., Li, W., He, J., Xu, Z., Peng, Y., Liu, Z., Zhang, Y., Li, P., Huang, P., & Wang, R. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104217 · Cited by 2
The role of artificial intelligence in advancing population-based cancer registration
Ding, S., Liu, M., Wang, H., Song, C., Zhao, L., Yang, Z., Wang, Y., Wang, Y., Cui, H., Liu, Z., et al. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.02.012 · Cited by 2
Self-citationVLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation
Nguyen, A. T., Byeon, K., Kim, K., & Kwak, J. T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-17611-0_2 · Cited by 2
Giarnieri, E., Lastrucci, A., Rícci, A., Bruno, P., & Giansanti, D. (2026) · Journal of Imaging · doi:10.3390/jimaging12030100 · Cited by 2
“Tool-augmented architectures, including the integration of LLMs with domain-specific visual models and smaller task-focused networks, represent a promising direction for responsible deployment (Lu et al., 2024 [68]; Ding et al., 2024 [65]).”
InfluentialZeng, G., Li, W., Mei, H., & Du, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1775750 · Cited by 2
Advances in predicting omics profiles from imaging data
Beachum, A. H., Xiao, X., Zhou, Y., Li, Q. Y., Xiao, G., & Xu, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag090 · Cited by 2
“Visual-language models like CLIP [27] have been adapted for use in pathology, enabling image captioning from histology slides, as demonstrated by CONCH [28] and PLIP [29].”
Zheng, Q., Mei, H., Wang, D., Liu, X., Wang, L., & Chen, Z. (2026) · Annals of Surgical Oncology · doi:10.1245/s10434-026-19471-x · Cited by 2
Data- and knowledge-driven multimodal learning in computational pathology: A comprehensive survey
Liu, M., Cai, C., Chen, D., Li, J., Li, J., Ming, W., & Xu, J. (2026) · EngMedicine · doi:10.1016/j.engmed.2026.100132 · Cited by 2
Li, Y., Lin, Z., Zhang, Q., Ding, X., Yang, H., Pi, L., Yuan, W., Wen, Y., Guo, L., Zhang, Q., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104060 · Cited by 2
Hou, W.-T., Pu, Z.-F., Xu, Z.-Y., Wu, A.-H., Liu, Z., Zhao, K., Duan, C.-L., Guo, J., Chen, K., Qiu, S.-Q., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104069 · Cited by 2
He, Q., Tan, H., Xiao, B., Peng, X., Peng, C., Tan, Y., Liu, Y., Cao, Y., Lv, F. J., Zhao, W., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01415-z · Cited by 2
“UtilizingCONCH,aCLIP-basedvision-languagemodel, wedesignedprognosticinformationastextpromptsandcalculatedthe probabilityofeachpatchaligningwithcorrespondingpromptstogeneratea tumorphenotypeheatmap,whichfunctionedasaglobalknowledge-guided slide representation 17 .”
He, Q., Li, J., Guan, T., Ma, Y., Zhao, Z., Wang, Y., Chen, H., Xu, Y., Ge, S., Zhang, Y., et al. (2026) · Advanced Science · doi:10.1002/advs.202520580 · Cited by 3
“Three general-purpose pathology foundation models were selected: UNI, PLIP, and CONCH [ 69–71 ].”
Mallya, M., Mirabadi, A. K., Farahani, H., & Bashashati, A. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00331 · Cited by 2
“GPFM used UNI [7], CONCH [30] and Phikon [17], while AM-Radio used CLIP [38], DI-NOv2 [37], and SAM [26] as their teacher models.”
Foundation models in computational pathology: methods, applications and clinical implications
Sali, R., Aschenaki, Y., Leveillee, R. J., Baba, F., Tessema, L., Dixon, C. M., Bachhamba, K., & Zhang, D. Y. (2026) · BMJ Oncology · doi:10.1136/bmjonc-2026-001102 · Cited by 4
Lou, P., Zhu, Y., Chia, N., Kumari, R., Yang, W., Wang, Y., Novotny, B. C., Winham, S. J., Guo, R., Goode, E. L., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02741-z · Cited by 2
“In this study, we replaced the original CNN-based encoder used by CLAM with CONCH 28 , a pretrained encoder speci fi cally optimized for histopathological images, to enhance the representation of patch-level morphologic features.”
InfluentialNakashima, M., Qiu, J., Huang, P., Lee, J., Chen, P. M., Grimm, R. A., Nguyen, C. T., Kim, B.-H., Zhao, D., Kwon, D. H., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73022-2 · Cited by 2
“These vision-language frameworks have been applied to biomedical data including chest X-ray, single view echocardiography, and pathology slides 16 – 18 to improve domain-speci fi c performance beyond generalist models.”
A comprehensive survey of computer vision methods for spatial transcriptomics
Zhu, J., Deng, R., Guo, J., Yao, T., Lu, S., Qu, C., Xiong, J., Zhu, Y., Lu, Z., Yang, Y., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag255 · Cited by 5
Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
Parkinson, G., Patel, R. D., Bergstrom, C. P., Vadasz, B., Amgad, M., Cooper, L. A. D., Sparano, J. A., & Lu, J. (2026) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk-26-520716 · Cited by 4
Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging
Nia, N. G., Manwar, R., & Avanaki, K. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04241-3 · Cited by 2
Abdullah, A., Shahid, M., Ather, M. A., Fatima, Z., Sanchéz-Mejorada, C. G., Torres-Ruiz, M. J., Quintero, R., Mata, F., & Zagal-Flores, R. (2026) · Cancers · doi:10.3390/cancers18121952 · Cited by 2
“[40] Pathologyfoundation”
PathFound: An agentic multimodal model activating evidence-seeking pathological diagnosis
Hua, S., Wu, J., Shen, T., Hu, K., Huang, Z., Ni, S., Zhang, Z., Li, Y., Wang, Z., & Zhang, X. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104200 · Cited by 4
MerMED-FM: Multimodal, Multi-Disease Medical Imaging Foundation Model
Zhou, Y., Quek, C. W. N., Zhou, J., Wang, Y., Bai, Y., Gutiérrez, L., Ke, Y., Yao, J., Teo, Z. L., Ting, D. S. J., et al. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.101007 · Cited by 2
Deep Learning-Based Automated Measurement of Murine Bone Length in Radiographs
Rong, R., Denton, K., Jin, K. W., Quan, P., Wen, Z., Kozlitina, J., Lyon, S., Wang, A., Wise, C. A., Beutler, B., et al. (2024) · Bioengineering · doi:10.3390/bioengineering11070670 · Cited by 2
“The advent of computer vision methods, especially ones based on deep learning, has enabled the automation and accurate analysis of medical images [4–10].”
MethodSimplified Artificial Intelligence Terminology for Pathologists
Zabihollahy, F., Mankaruos, M., Mohareb, M., Youssef, T., Soleymani, Y., & Yousef, G. M. (2025) · Diagnostics · doi:10.3390/diagnostics15131699 · Cited by 2
“A couple of foundation models in the field of pathology include Virchow, GigaPath, UNI, and CONCH [43–46].”
Benchmarking Histopathology Foundation Models in a Multi-center Dataset for Skin Cancer Subtyping
Meseguer, P., del Amor, R., & Naranjo, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_2 · Cited by 2
Liu, H., Farahani, H., Li, X., Xie, Y., & Bashashati, A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3595101 · Cited by 2
“…First, back-bone networks have evolved from convolutional neural network (CNN)-based architectures (e.g., KimiaNet [25], Ciga [26], and REMEDIS [27]) to ViT-based ones (e.g., UNI [28], PLIP [29], CONCH [30], GigaPath [31], and Phikon [32]), as well as integrated structure (e.g., CTransPath [33]).”
Full Conformal Adaptation of Medical Vision-Language Models
Silva-Rodríguez, J., Fillioux, L., Cournède, P.-H., Vakalopoulou, M., Christodoulidis, S., Ayed, I. B., & Dolz, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96625-5_19 · Cited by 2
Min, W., Xue, S., Zhu, F., Xu, T., Wang, C., & Xie, H.-B. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010087 · Cited by 2
Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions
Lucassen, R. T., Moonemans, S. P. J., van de Luijtgaarden, T., Breimer, G. E., Blokx, W. A. M., & Veta, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_48 · Cited by 2
Jiang, J., Liu, J., Wu, H., Wu, Y., Wang, Z., Bi, Q., & Zheng, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754838 · Cited by 3
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
