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 801–900 of 968
Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging
Qin, S., Zhao, N., Shen, L., Zhang, Y., & Ge, S. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1762586
“CLIP aligns visual and linguistic representations through contrastive learning, enabling tasks like image retrieval via natural language queries (45–47).”
Patch-Level Tissue Context Improves Learning from Frozen Pathology Foundation Model Embeddings
Dip, S. A., & Zhang, L. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3820870
“We evaluate both pathology-specific and general-purpose visual encoders, including Prov-GigaPath [32], Virchow2 [29], UNI [7], CTransPath [30], CONCH [20], DINOv2 ViT-L [24], MAE ViT-L [12], ViT-Large [8], and ResNet50 [13].”
Jumaah, N. S., Jabbar, M. S., Abbood, S. H., Ayesha, N., Mohamed, M. B. A., & Khan, M. I. (2026) · Computational and Systems Oncology · doi:10.1002/cso2.70022
Hellmann, F., Anokhin, M., Schimmler, P., Tippner, D., Plontke, S., Kresbach, C., Mensah, M. A., André, E., & Harder, A. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100700
Alkhatib, A. J. (2026) · Research Square · doi:10.21203/rs.3.rs-10463473/v1
Manandhar, S., Wu, Y., Pathak, T., Margulies, K. B., Madabhushi, A., & Peyster, E. G. (2026) · Cardiovascular Research · doi:10.1093/cvr/cvag166
“While computational pathology has transformed oncology through advanced segmentation pipelines 24,25 and foundation models, 26,27 comparable tools for cardiac histopathology are lacking, limiting the translation of histological features into reproducible, clinically meaningful metrics.”
Pelissier-Combescure, M., Villemin, J.-P., Bonzom, D., Berger, C., Estoup-Streiff, C., Lakhman, Y., Woitek, R., Colombo, P., Lin, Z., Li, H., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1874918
“CNN-based encoders are used in earlier studies (56, 100, 109), while more recent works adopt ViT-based foundation models pre-trained on large pathology datasets, such as UNI (26) or CONCH (27), signi fi cantly reducing the need for large task-speci fi c training cohorts, a critical advantage given the limited cohort siz…”
InfluentialGiner, F., Pastor-Naranjo, Á., Meseguer, P., del Amor, R., Gambarotti, M., Righi, A., López‐Guerrero, J. A., Navarro, S., Mayordomo‐Aranda, E., Llombart-Bosch, A., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27156864
“Among these, models such as CONCH [26] have been further pretrained on histopathological datasets, enabling them to learn domain-specific representations of tissue morphology.”
Mu, B., & Yi, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31920-3_10
Merging-Based Federated Learning for Lifelong Whole Slide Image Analysis with Vision-Language Models
Bui, D. C., Pham, H. L., Le, V. T. D., Nguyen, M. K., & Nakashima, Y. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-2590-3_45
Detecting rare whole slide image via cross-space multi-prompt generation and collaboration
Yu, J., Du, T., Li, Y., Ma, T., Wang, Y., Wu, T., Tam, S., Huang, H., & Xu, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133840
Chen, Y.-H., Xu, Q., Yao, H., Liu, K.-Z., Gui, C., Fu, L., Wang, Y., Zhu, J., Li, J., Chen, M., et al. (2026) · Cancer Medicine · doi:10.1002/cam4.72161
“Among the five candidate foundation models: UNI [14], CHIEF [17], CONCH [15], Virchow [16] and Prov-GigaPath [18], UNI and CHIEF were selected for their superior AUROC performance (Figure S4).”
Spatially resolved tissue architecture and computational pathology in pancreatic cancer
Bae, S.-W., Tsirigos, A., Min, J., & Maitra, A. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01782-4
“Furthermore, multimodal frameworks that jointly analyse histopathology images, together with clinical and molecular data, are beginning to bridge morphology and functional biology, facilitating clinically actionable insights 25,26 .”
Atiya, S., Liang, J., Ofori-Atta, K., Peng, M., Wang, H., Zhou, Y., Patel, A., Edgertion, M., Zhao, J., & Pamuksuz, U. (2026) · Cancers · doi:10.3390/cancers18152475
YiJiang, D., Yuanwei, J., & Zhang, W. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1825286
“The generated clinical narrative is encoded using the frozen text encoder of the CONCH foundation model (30).”
Alawode, B., Abdalla, M. A., Mahapatra, D., Naseer, M., & Javed, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36969-7_9
Rosbach, E., Ammeling, J., Bertram, C., Riener, A., & Aubreville, M. (2026) · Veterinary Pathology · doi:10.1177/03009858261472493
FusionSurv: A multi-modal fusion representation network for pan-cancer survival analysis
Gao, R., Yang, Z., & Liu, Z. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111197
Ansari, S., Faraji, N., Topham, L., Khan, W., Shehada, D. J. M., Mahmoud, S., Tawfik, H., & Hussain, A. J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1873938
Jansson‐Knodell, C. L., Savage, E. C., Poissant, K., Kerosky, M., Yu, I., Fong, R., Dalton, J. E., Robertson, S., & Rubio‐Tapia, A. (2026) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2026.07.021
Morphology semantics-guided vision language alignment for cervical cell image classification
Lei, J., Feng, Z., Sun, X., Ye, J., Yuan, Z., Yu, J., & Zhang, J. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114606
Zhang, C., Li, B., Zhang, B., Zeng, Y., Yi, Y., & Lv, J. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag598
“Specifically, we employ two state-of-the-art encoders, UNI (Chen et al. 2024) and CONCH (Lu et al. 2024), both pretrained on large-scale whole-slide image datasets.”
The Intersection Between Neuropathology and Artificial Intelligence: A Review
Frassetto, F. P. (2026) · Sage Open Pathology · doi:10.1177/30502098261472442
“52-54 Many models are trained on relatively small or institution-specific datasets, raising concerns about generalizability and dataset bias.”
Safa, I., Hazoglu, M., Galera, P. K., Vanderbilt, C., Kamali, A., Goldgof, G. M., Veeraraghavan, H., Jiang, J., Ardon, O., Geneslaw, L., et al. (2026) · medRxiv · doi:10.64898/2026.08.11.26360085
Ziaullah, K., Mozumder, M. A. I., & Kim, H. C. (2026) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet65726.2026.11632529
“Three backbone architectures, ResNet-50 [17], UNI [18], and CONCH [19], were used to obtain patch-level features subsequently.”
CSFMIL: Whole slide image classification with two-stage cross-scale fusion
Li, S., Luo, Y., Chen, Z.-N., Chen, F.-J., Hu, K., & Gao, X.-P. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134807
Evaluating Demographic Fairness In Histopathology Foundation Models
García-de-la-Puente, N. P., López-Pérez, M., & Naranjo, V. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630439
Akebli, H., & Mea, V. D. (2026) · Journal of Imaging · doi:10.3390/jimaging12080381
“CONCH [29] differs from the other FMs by employing vision-language pretraining; in this study, only its unimodal visual encoder was used.”
Time-Aware Semantic Prototypes for Weakly-Supervised Endomicroscopy Video Classification
Carretero, I., Meseguer, P., Zammarchi, I., Pugliano, C. L., Santacroce, G., Kolawole, B. B., Chaudhari, U. B., del Amor, R., Grisan, E., Iacucci, M., et al. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630163
Cross-Domain Image Synthesis: Generating H&E from Multiplexed Biomarker Imaging
Saurav, J. R., Nasr, M. S., & Luber, J. M. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-7425-4_7
Hassan, A., Siddiqui, N., Khan, B., Malik, S., & Ali, H. (2026) · Pharmacophore · doi:10.51847/lzb9klxouu
Baviello, C., Capuano, D. M., & Verna, R. (2026) · Life · doi:10.3390/life16081346
“VLMs bridge this gap by intrinsically linking histological features with textual descriptions, enabling “zero-shot” classification [52].”
Meng, B., Wang, J., & Liu, J. (2026) · Electronics · doi:10.3390/electronics15163691
Assessing Foundation Models for Computational Pathology in Endometrial Cancer
van den Berg, N., Volinsky-Fremond, S., Wolf, J. B., Schoenpflug, L. A., Andani, S., Ørtoft, G., Jobsen, J. J., Lutgens, L. C. H. W., Powell, M. E., Mileshkin, L., et al. (2026) · Modern Pathology · doi:10.1016/j.modpat.2026.101070
Liu, Y., Dong, Y., Zhang, G., Wu, Y., Zhang, S., Chen, Y., Cao, R., Wang, R., Yin, C., & Li, Y. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1863777
Artificial intelligence in oncology: linking biological discovery to clinical utility
Kuchana, S. K., Alahari, N. V., Kode, R., Jalapu, K. R., & Alahari, S. K. (2026) · Molecular Cancer · doi:10.1186/s12943-026-02771-x
“…Virchow used approximately 1.5 million slides with emphasis on rare cancers; Prov-GigaPath used 1.3 billion tiles from 171,189 slides; and CHIEF combined tile-and slide-level pretraining across 60,530 slides and validated on 19,491 slides from independent cohorts [53–55, 78, 79] (Table 2).”
InfluentialLi, J., Kuang, H., Liu, J., He, M., Yue, H., & Wang, J. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104278
From data silos to integrated diagnosis: multimodal machine learning in glioblastoma
Shirazi, A. Z., Day, B. W., Gan, H. K., & Gomez, G. A. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1912365
Bartaouch, K., & Jourani, R. (2026) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc71566.2026.11650391
“Web Data [6, 9, 35, 39 Researchers use Recall@K and F1-Score [5, 9, 20] for models that meant to search for images, however, automated metrics can still miss fine details, this is why human judgment having real people rate the descriptions remains the best way to ensure the AI is being truthful and…”
Learning positive vision-language noise for few-shot histopathology image classification
Meng, Y., Guo, H., Manjia, L. I., & Liu, X. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134915
Huang, Y., Yuan, Y., Ye, W., & Ming, W. (2026) · Mathematics · doi:10.3390/math14173059
“Attention-based multiple instance learning (MIL) frameworks [13–15] can extract prognostic information from gigapixel whole-slide images (WSIs) under weak supervision, and pathology foundation models pre-trained on large histopathological corpora [16–18] have substantially improved feature quality.”
ECHO: A unified framework for efficient cell-graph construction for breast histology classification
Tommasino, C., Russo, C., & Rinaldi, A. M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111294
Al-Toub, M. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48090859
“…patches from over 100,000 whole-slide images and was evaluated across a broad range of computational pathology tasks, while the CONCH visual language model combined histopathology images with biomedical text to support classification, segmentation, captioning, and image–text retrieval [31,32].”
Chung, W., Park, Y., Kwak, Y., Lee, H. D., Kim, B., Shin, C. M., Nam, Y., & Lee, H. S. (2026) · Laboratory Investigation · doi:10.1016/j.labinv.2026.106161
Multi-instance multi-label learning: Theory, taxonomy, and modern methods—A critical survey
Ahmed, S. U., Tahir, M. A., & Sheikh, M. W. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134896
IA et pathologie computationnelle
Cheng, Y., & Caldéraro, J. (2026) · Annales de Pathologie · doi:10.1016/j.annpat.2026.07.001
Grading of Castleman Disease Histopathology with an Attention-Based Multiple Instance Learning Model
Morrison, M. J., Alnoor, F., O’Fallon, B. D., Hutchings, A., Dewey, M., English, P., Rangel, A. E., Zuromski, L. M., Knight, K., Bowen, A., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16172750
“…models, including UNI and multimodal vision-language models such as CONCH, similarly report improved data efficiency and cross-domain generalization; systematic comparisons of encoders in lymph node pathology and non-neoplastic inflammatory disorders represent an important next step [20,23,24].”
Liu, Z., Su, L., Yu, J., Geng, X., Wang, L. S., Wang, J., & Liu, J. (2026) · Biomedicines · doi:10.3390/biomedicines14091935 · Cited by 1
Dalani, T., Jerry, W. K. H., Wu, J., Fang, X., Feng, T., Xie, F., Huang, M., Liang, Y., To, K. F., Kang, W., et al. (2026) · Cancers · doi:10.3390/cancers18172802
“CONCH is a model developed by Lu et al. that supports image-text alignment, cross-modal retrieval, classification, and captioning [74].”
Self-citationMohammed, O. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22167477
Xu, Z., Huang, Q., Tian, G., Liu, Q., & Wu, K. (2026) · Techniques and Innovations in Gastrointestinal Endoscopy · doi:10.1016/j.tige.2026.250988
Li, S., Shang, H., Zhou, J., Lei, M., Zheng, F., Wang, J., Yang, D., Bao, W., & Wang, Y. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104286
Chen, Y., Chen, X., Hu, J., Tan, M., Wang, Y., Nie, L., & Wang, T. (2026) · Talanta · doi:10.1016/j.talanta.2026.130540
Generative large language models in medicine: a scoping review of recent methodological advances
LI, F., Li, J., Cao, W., Li, H., Li, Y., Sun, Z., Chen, D., Duan, T., Li, P., Tizhoosh, H., et al. (2026) · npj Health Systems · doi:10.1038/s44401-026-00137-5
“From visual-language foundation models advancing computational pathology 7,8 to prompt-engineered chatbots supporting outpatient interactions 9 , from automated summarization of clinical documentation 10 – 12 to adaptive decision-support systems 13 , LLMs have been rapidly explored across the…”
Mohammed, O. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22257735
Godson, L., Breen, J., Wright, A. I., Magee, D., Bansal, D., Treanor, D. E., & Clarke, E. L. (2026) · Frontiers in Medical Technology · doi:10.3389/fmedt.2026.1835452
“Dividing the dataset into five groups, we found that the subgroup with the second-highest number of patches (patch range [19,070, 26,771], Table 2) achieved the highest AUROC across all models.”
Bartusik‐Aebisher, D., Czech, S., Szpara, J., Paul, A., Xavierselvan, M., & Aebisher, D. (2026) · Applied Sciences · doi:10.3390/app16178771
Large language models utilizing digital pathology workflows: Impact on pathology practice
Huang, Z., Yao, J., & Bao, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00005-4
Zhang, Y., Liu, Q., Zhao, Z., Chen, Q., Zheng, S., & Bai, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111384
The role of pathology foundation models in cancer diagnosis, biomarker discovery, and prognosis
Embaye, K. S., Tubelleza, R., Pavilion, G. L., Kilgallon, A., & Kulasinghe, A. (2026) · Trends Open · doi:10.1016/j.treopn.2026.08.006
Generative artificial intelligence technologies in pathology
Ullah, E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00013-3
Applications of artificial intelligence in surgical pathology
Li, Z., Liu, Y., MacDonald, W., Sun, S., Chen, W., Patel, A. U., Parwani, A. V., & Satturwar, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00008-x
Self-citationPixelomics: The pixelome as a foundation of precision pathology
Ullah, E., Hu, J., Rashidi, H., Pantanowitz, L., Li, Z., & Parwani, A. V. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00016-9
Self-citationAhmed, A. A., Adeshina, S. A., Aleshinloye, Y. A., Ogundokun, R. O., & Owolawi, P. A. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100475
Alhazmi, A. H. (2026) · Diagnostics · doi:10.3390/diagnostics16172869
“UNI was developed as a general-purpose pathology representation [6], while CONCH introduced vision-language pretraining on more than one million histology image–caption pairs [7].”
AI-assisted multimodal data integration for precision oncology
Chera, A., Chirilaș, A.-M., El-Sabeh, A., Cretu-Stancu, M., & Bucur, O. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1917442
“Frameworks such as CONCH (Lu et al., 2024) and HONeYBEE (Tripathi et al., 2025) integrate WSIs with clinical and genomic data, utilizing cross-modal attention in order to achieve holistic diagnostic data fusion.”
InfluentialInsights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer
Huang, H.-Y., Kow, A. W. C., Fan, Z., & Lv, G. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1871770
“…HPC: for BTC and PDAC, where single-institution cohorts rarely exceed a few hundred cases, fi ne-tuning a foundation model requires substantially fewer labeled examples than training from scratch, potentially overcoming the cohort-size bottleneck repeatedly fl agged in this review (73, 74, 101, 102).”
InfluentialDomain-generalizable oral cancer classification: bridging multi-center histopathological datasets
Hua, Z., & Zhou, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1951350
“UNI (67), Virchow and Virchow2 (68, 69), Phikon (70), CHIEF (71) and the vision – language model CONCH (72) all report strong transfer with frozen features and little supervision.”
Huang, P., Huang, Y., Chen, Y., Yeung, M., Jiang, Y., & Yu, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37432-5_13
When pathology segmentation learns to listen
Shen, W. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01048-z
Wang, X., Zhu, Y., Su, S., Guo, M., Li, X., Zhong, X., Ding, Y., Zhao, S., & Dai, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111416
Harnessing SSL for Segmentation in 3D Microscopy with Noisy Labels and Hard Patches
Xu, T., Attarpour, A., Patel, S., Yu, F., Rozak, M. W., Stefanovic, B., Martel, A. L., & Goubran, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37435-6_16
MedCure: Medical Data Curation for Efficient Vision–Language Pretraining
Wang, C., Liu, F., Liu, Y., Chen, Y., Tian, Y., & Fang, L. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3730839
“Beyond CXR, vision-language pretraining has also been explored in other medical imaging domains such as histopathology [39] and dermoscopy [40].”
PyraE2E: Enhancing End-to-End WSI Analysis via Cross-Scale Super-Resolution
Lin, Y., Liu, Y., Zhang, W., Fan, Y., Wang, Z., Shen, D., Fei, L., Liu, X., & Xu, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37261-1_10
Nguyen, B.-D., Hoang, V.-D., & Nguyen, H. D. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00871-8
Artificial Intelligence for Thyroid Histopathology Image Analysis: A Systematic Review
Tao, G., Yaakob, R., & Abdipoor, S. (2026) · Journal of Artificial Intelligence · doi:10.3923/jai.2026.78.93
Scaling Whole-Slide Pathology Foundation Model Pretraining with Billions Off-the-Shelf Tokens
Li, H., Shui, Z., Zhu, C., & Yang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37335-9_21
Alzoubi, H., Shatarah, A., Sanajleh, A., & Sughayer, M. A. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1867968
“Foundation Model Integration: Using computational pathology foundation models (37, 38) as feature extractors for improved regression accuracy.”
Xu, Y., Nasir, M., Matos-Romero, V., Chen, T., Hruban, R. H., Weeks, W. B., Dodhia, R. M., Ferres, J. L., & Kiemen, A. (2026) · Cancers · doi:10.3390/cancers18183004
“After background, artifact, and annotation-based filtering, we used the pretrained CONtrastive learning from Captions for Histopathology (CONCH) model [45] to remove tiles that lacked morphology associated with the primary diagnosis.”
Han, A., Jolley, A. F., Butler, L. M., & Chen, W. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag679
“Patch-level features were primarily extracted using CONCH (ViT-B/16), a vision-language foundation model pretrained on pathology images (Lu et al. 2024).”
InfluentialFoundation Models in Medical Imaging: A Systematic Review, Taxonomy, and Future Research Agenda
Ghanim, R. (2026) · Academia Open · doi:10.21070/acopen.12.2027.15324
“Volumetric models were developed along the same lines as SAM-Med3D (Wang et al., 2025) and SegVol (Du et al., 2024); large multi-structure tools were created like TotalSegmentator (Wasserthal et al., 2023); self-supervised pathology encoders such as UNI (Chen et al., 2024); vision-language and multimodal systems like…”
Noori, A., Fishman, N., Fang, A., Fesser, L., & Žitnik, M. (2026) · Cell · doi:10.1016/j.cell.2026.08.032 · Cited by 3
Nguyen, N. H., Lý, K. N., & Le, N. Q. K. (2026) · Computers · doi:10.3390/computers15090626
Kilim, O., Pipek, O., Sztupinszki, Z., Dióssy, M., Prósz, A. G., Naceur‐Lombardelli, C., Veeriah, S., Moore, D., Jamal‐Hanjani, M., Hackshaw, A. K., et al. (2026) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2026.101614
Mallya, M., Mirabadi, A. K., Farahani, H., & Bashashati, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37032-7_10
Role of foundation models in data-driven tissue diagnostics
Bilal, M., Aadam, Raza, M., Alsuhaibani, A., Altherwy, Y. N., Alabduljabbar, A., Almarshad, F. A., Golding, P., & Rajpoot, N. (2026) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2026.101504
Chen, J., Cai, L., Wang, Z., Huang, Y., Jiang, S., Huang, S., Wang, H., & Zhang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37422-6_28
Foundation-Guided Hybrid CNN–Transformer for Confidence-Aware Colon Histopathology Classification
Rathore, N. P. S., Tandon, R., Sharma, S., & Agrawal, S. (2026) · Proceedings of the National Academy of Sciences India Section A Physical Sciences · doi:10.1007/s40010-026-01228-6
A universal visual foundation model for computational cytopathology
Zheng, X., Zheng, K., Wang, J., Zhang, X., Li, K., Zhao, Z., Li, C., Chen, H., Wang, R., Lai, J., et al. (2026) · Nature Cancer · doi:10.1038/s43018-026-01240-0
“Early visual foundation models demonstrated promise in histopathology, and subsequent multimodal systems have extended their reach to integrate text, images and structured clinical data, enabling diagnostic reasoning, collaborative workflows and scalable deployment 3–8 .”
Yang, S., Sui, F., Liang, Z., Liu, Q., Fu, B., Dong, H., & Wang, Z. (2026) · International Dental Journal · doi:10.1016/j.identj.2026.111098
Zhang, Z., Tang, R., Zhao, F., Zhang, H., Chu, T., Zhang, Y., Yu, G., Wang, Q., Li, X., & Mao, N. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104976
Yuan, L., Yin, Z., Han, Y., Zhang, J., Grzegorzek, M., & Li, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_23
ConVL: Interpretable Concept-Guided Vision-Language MIL for Survival Analysis in Whole Slide Images
Li, J., Kuang, H., Liu, J., Yue, H., He, M., Li, X., & Wang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_8
One Slide, Many Views: Unifying Complementary Foundation Model Perspectives for WSI Analysis
Wang, Y., Xu, Y., Yang, S., Bie, Y., Ma, J., Zhou, F., & Chen, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37321-2_27
Castellini, A., Tangherloni, A., & Buffa, F. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39028-8_39
Chen, Z., Yu, L., Lin, W., Chen, R., Magnier, B., Wang, X., & Wang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_28
Self-citationMultimodal prototype alignment for semi-supervised pathology image segmentation
Fu, M., 付方磊, Ling, X., Yuan, H., Guan, T., He, Y., & Zhu, L. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111514
Jung, K. J., Rout, S. S., Qiu, J., Cho, S., McDonough, E., Ghose, S., Chadwick, C. M., West, R. B., Brooks, J. D., Ginty, F., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38172-9_18
Di-PACT: Discrete Prognosis via Aligned Co-Topology of Pathways and Tumor Microenvironment
Cai, Z., Li, F., Cai, Q., & Zhang, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38172-9_19
Cross-Cancer Expert-Routing Knowledge Transfer in Federated Prognosis Prediction
Wang, S., Li, J., & Wang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38172-9_16
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
