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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.
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“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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“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]
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“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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Vadori, V., Peruffo, A., Graïc, J., Finos, L., & Grisan, E. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253185 · Cited by 5
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I-Viewer: An Online Digital Pathology Analysis Platform with Agentic-RAG AI CopilotPreprint
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“Next, pathology language models such as PLIP 34 , Quilt-Net 35 , and CONCH 36 , which are trained on both pathology slides and paired text, have demonstrated signi�cant performance in vision-based tasks as well.”
Wong, B., & Yi, M. Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5362908 · Cited by 2
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“After the diagnosis by the pathologists, according to the level of cell differentiation of the tumor, epithelial tissue arrangement, cancer cell nests, histopathology mitotic images, etc. , the core and borderline sites of the lesion are selected and the histopathology pictures are preserved after…”
InfluentialBuzzanca, G., Pala, C., He, J., Hofstraat, R., Tammaro, A., van Midden, D., Buelow, R. D., Hölscher, D. L., Muehlfeld, A., Koeller, M. C., et al. (2026) · medRxiv · doi:10.64898/2026.08.28.26360029 · Cited by 2
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Guetarni, B., Windal, F., Benhabiles, H., Chaibi, M., Dubois, R., Leteurtre, E., & Collard, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77786-8_15 · Cited by 1
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InfluentialFoundation Models for Slide-Level Cancer Subtyping in Digital Pathology
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Multi-Scale Multiple Instance Learning for Lymph Node Metastasis Prediction in Early Gastric Cancer
Jin, S., Xu, H., Zhu, Y., Gao, X., Wang, M., & Cong, F. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253787 · Cited by 1
“Instance-level embedding is performed using a pretrained foundation model CONCH, denoted as E s (•) , which was developed and trained using over 1.17 million histopathology image – caption pairs [13].”
InfluentialECP-KD: Efficient Computational Pathology Heterogeneous Model Fusion Using Knowledge Distillation
Ni, T., Zhu, H., Han, Y., Lai, C., Chen, J., & Pan, Y. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254751 · Cited by 1
“Recent development in pre-trained computational pathology models, such as CLAM [1], UNI [2], CONCH [3], and CONCH1.”
Research progress of universalvision foundation models in computational pathology
Fu, Z., Da, Q., Wang, W., Wang, C., Zhong, D., Wang, S., & Wang, L. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-5045 · Cited by 1
A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics
Zheng, K., Zheng, X., Wang, J., Zhang, X., Chen, S., Chen, Q., Fu, S., Xie, D., Wang, R., Lai, J., et al. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-25-2445 · Cited by 2
“The recent proliferation of large-scale foundation models has further heightened the demand for diverse, high-quality datasets, which are particularly difficult to obtain in cytopathology due to the rarity of certain cases and challenges in data preservation (10, 11).”
Saeidi, N., Torbati, N., Woitek, R., & Mahbod, A. (2025) · Bioengineering · doi:10.3390/bioengineering12121332 · Cited by 1
“For pre-trained models, we employ the same CNNs and ViT-Base as baseline models, along with three pathology foundation models: CONCH [39], UNI [38], and UNI-2 [38,40].”
MIRAGE: Medical image-text pre-training for robustness against noisy environments
Cheng, P., Huang, Y., Lin, L., Lyu, J., Wong, K. K. Y., & Tang, X. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103912 · Cited by 1
Subtype classification of gastric spindle cell tumors in whole slide images
Lee, H., Lee, Y. J., Kim, E., Lee, J., Ahn, S., & Lee, S. H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111410 · Cited by 1
Ji, X., Zelić, R., Aspegren, O. P., Mulliqi, N., Fiorentino, M., Giunchi, F., Molinaro, L., Boman, S. E., Szolnoky, K., Liu, L. X., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2025-111361 · Cited by 2
Farag, M. S., Oghbaei, N., Hussain, J., Lametti, A., Wassim, K., & Brimo, F. (2025) · Histopathology · doi:10.1111/his.70086 · Cited by 1
“17,18 Based on these studies, the latest WHO blue book edition recommended restricting a pTaHG diagnosis to tumours containing ≥ 5% HGc.”
Chinese expert consensus on precision diagnosis and treatment of refractory tumors
Wang, L., Zhang, Y., Shang, Y., Lu, Y., Hu, H., Wang, G., & Wang, H. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105109 · Cited by 1
Kaczmarzyk, J., Alsten, S. C. V., Cozzo, A. J., Gupta, R., Koo, P. K., Troester, M. A., Hoadley, K. A., & Saltz, J. H. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02334-2 · Cited by 2
Knowledge-guided multi-modality transformer for multi-label genetic mutation prediction
Huang, G., Wu, C., Li, M., Chang, X., Sun, Y., Xing, L., Liang, X., Lin, L., Yang, G., & Zhao, S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113047 · Cited by 1
Yao, J., Li, S., Liang, P., Xu, X., Elder, D. E., & Huang, Z. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02357-3 · Cited by 4
“Pathology vision-language foundation models such as PLIP 34 , CONCH 35 , PathGen 36 , and MUSK 37 combine visual understanding with language-based reasoning, enabling the integration of both image features and textual medical knowledge.”
HMHGT: Hierarchical Multi-Scale Hypergraph Representation for Histopathology Whole Slide Images
Wang, S., Jiang, L., & Qiu, C. (2026) · IEEE Access · doi:10.1109/access.2026.3655215 · Cited by 1
“…as the ‘‘gold standard’’ in pathological diagnosis, inherently contain cross-scale information ranging from cellular morphology (nuclear features, cytoplasmic characteristics) to tissue architecture (structural patterns, tumor-stroma interfaces, infiltrating immune cells) [1], [2], [3], [4], [5].”
Ding, M., Chen, W., Luo, X., Zhong, H., & Shen, L. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113126 · Cited by 1
Feng, Y., Yi, J., Xia, M., Zhang, X., Cai, W., & Zhang, X. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3657524 · Cited by 1
“Specifically, the models trained on unimodal datasets are Virchow2 (ViT-H / 14) [24] and Prov-GigaPath (ViT-G / 14) [26], the models trained on multimodal datasets comprise CHIEF [27], CONCH (ViT-B / 16) [29], TITAN [30], and UNI2 (ViT-H / 14) [22].”
Multimodal learning for scalable representation of high-dimensional medical data
Alsaafin, A., Shafique, A., Alfasly, S., Kalari, K. R., & Tizhoosh, H. R. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1709277 · Cited by 1
“Beyond pathology, large-scale vision–language models such as CLIP, e.g., CONCH [23], align images and text via contrastive training on hundreds of millions of pairs, enabling zero-shot transfer and cross-modal retrieval in natural-image domains.”
Barua, B., Chyrmang, G., Bora, K., Kakoti, L., Ahmed, G. N., Neog, D. R., & Saikia, M. J. (2026) · Journal Of Big Data · doi:10.1186/s40537-025-01358-5 · Cited by 1
“094 ), whereas Virchow2 [20] and CONCH [22] degraded significantly (IoU ≤ 0 .”
InfluentialZero-Shot Grounding of Histological Images Using Single-Cell Transcriptomic Profiles
Wu, C., Zuo, M., & Xie, L. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356619 · Cited by 1
“Gene encoders include FC layers, scBERT [26], and scGPT [27]; image encoders include ResNet50/152, ViT [28], and histology models CONCH [29] and Loki [30].”
Cell type annotation using large language models (LLMs) and CytoAnalyst
Nguyen, K., Tran, D., Nguyen, P., Ro, S., Bya, P., & Nguyen, T. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag001 · Cited by 1
“With the advancement of AI, especially Large Language Models (LLMs), we have the opportunity to automate many steps in the interpretation of complex biomedical data (Jumper et al. 2021, Lu et al. 2024, Boehm et al. 2025).”
Meng, B., Yang, W., Long, X., Wang, Y., Dang, K., Zheng, Y., & Liu, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356784 · Cited by 1
“Building on these efforts, CONCH [16] was pretrained on over 1.17 million expert-curated histopathology image–caption pairs and achieved state-of-the-art performance across 14 diverse downstream tasks.”
InfluentialTang, E., Xia, H., Yuan, Z., Zhao, Y., Wang, S., Ye, Z., Gao, S., Zhou, Z., Zhao, Y., Zeng, J., et al. (2026) · Biomedicines · doi:10.3390/biomedicines14020348 · Cited by 1
“Firstly, we individually evaluated various open-source, pre-trained histopathology image encoders (including CONCH [26], CTransPath [27], GigaPath [28], Phikon-v2 [29], ResNet50, UNI [30], and UNI2-h [31]) using primary ovarian cancer WSIs.”
Gyarteng, E. S. A., XiaoLing, Z., & Ukwuoma, C. C. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104679 · Cited by 1
Hao, X., Xu, H., Zhang, J., Xu, Q., Pölönen, I., & Cong, F. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104005 · Cited by 1
Zhang, C., Qu, L., Liu, X., & Song, Z. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102733 · Cited by 1
Zhao, Y., Ding, H., Zhang, X., Zhao, X., Zhao, S., Yang, P., Wang, D., Wang, S., & Xing, X. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102738 · Cited by 1
Niu, Y., Liu, J., Zhan, Y., Shi, J., Zhang, D., Reinius, M., Machado, I. P., Crispin‐Ortuzar, M., Wu, J., Li, C., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104008 · Cited by 6
Deep Learning–Enhanced Biomarker Interpretation on Cytology Cell Blocks
Xia, R., Littlefield, N. G., Park, C. Y., Bao, R., Cangiarella, J., Simsir, A., & Gu, Q. (2026) · American Journal Of Pathology · doi:10.1016/j.ajpath.2026.02.004 · Cited by 1
Cheng, Y., Azouzi, N., Laurent-Bellue, A., Guo, Z., Chung, T., Zeng, Q., Ghodsifard, A., Albrecht, T., Roessler, S. K., Boulagnon‐Rombi, C., et al. (2026) · Annals of Oncology · doi:10.1016/j.annonc.2026.02.018 · Cited by 1
Adaptive diagnostic reasoning framework for pathology with multimodal large language models
Hong, Y., Kao, K.-C., Edwards, L., Liu, N. T., Huang, C., Oliveira-Kowaleski, A., Hsieh, C.-J., & Lin, N. Y. C. (2026) · Communications Medicine · doi:10.1038/s43856-026-01491-z · Cited by 1
Morphology-based cytogenetic risk prediction in multiple myeloma from bone marrow smears
Gao, M., Yan, X., Chen, L., Ouyang, G., Liu, Y., & Chen, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1790130 · Cited by 1
“CONCH extends this paradigm by incorporating vision-language pretraining on 1.17 million histopathology image-caption pairs (26).”
InfluentialPulido-Arias, D., & Arias-Mendoza, N. (2026) · Journal of Imaging · doi:10.3390/jimaging12040144 · Cited by 1
“For each patch, we extracted a 512-dimensional feature vector using CONCH [19], a foundation model pre-trained on pathology data.”
Deng, X. G., Zhu, T., Wang, Y., Hu, B., Yuan, H., Yu, D., Zeng, F., Huang, Z., Jia, X., Gao, Y., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07954-0 · Cited by 1
“The rise of large-scale, pre-trained foundation models in medical imaging is changing the CPath landscape [27–32].”
Fully Automated Stain Quantification Framework for IHC Whole Slide Images in Breast Cancer
Yin, T., Lifrange, F., Denis, Z., Caluwé, A. D., Buisseret, L., Catteau, X., Legros, C., Reynaert, N., & Dhont, J. (2026) · Technology in Cancer Research & Treatment · doi:10.1177/15330338251407734 · Cited by 1
“The signi fi cantly larger model size of CONCH (with around ten times more parameters than a standard UNet) make full fi ne-tuning Technology in Cancer Research & Treatment computationally intensive, whereas partial fi ne-tuning fails to achieve performance comparable to CNNs.”
InfluentialPRET is a few-shot system for pan-cancer recognition without example training
Li, Y., Ning, Z., Xiang, T., Zhang, Q., Lin, Z., Yi, M., Feng, F., Zeng, B., Qian, X., Sun, L., et al. (2026) · Nature Cancer · doi:10.1038/s43018-026-01141-2 · Cited by 4
“TITAN36 was pretrained using visual self-supervised learning and vision – language alignment with CONCH (version 1.5)11, with focus on multi-modal slide-level representations.”
InfluentialPark, J. H., Park, Y., Lee, Y. M., Yang, S., & Yoon, J. H. (2026) · Cancers · doi:10.3390/cancers18071155 · Cited by 1
Vision–Language Models in Medical Imaging for Cancer Diagnosis: A Bibliometric Review
Wakili, M. A., Suleiman, A. B., Majikumna, K. U., Shehu, H. A., Kusetoğulları, H., & Sharif, M. H. (2026) · Bioengineering · doi:10.3390/bioengineering13040466 · Cited by 1
“…of Artificial Intelligence (AI), especially in Computational Pathology (CPath) and deep learning applications, has further improved tasks such as cancer detection, subtyping, survival prediction, adenoma detection rates, real-time polyp histology assessment, and skin cancer classification [11–13].”
Clinical Implementation of Artificial Intelligence Systems in Medical Imaging and Radiotherapy
Vienna International Centre, 100, P. B., Vienna, A.-1., Austria, 2600-0, T. +. (., & 2600-7, F. +. (. (2026) · IAEA human health series · doi:10.61092/iaea.9olq-aqa9 · Cited by 2
Ma, Y., An, B., Shen, A., Yuan, M., Duan, M., & Wang, M. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02187 · Cited by 1
Qiu, Z., Chao, H., Lin, T., Chang, W., Yang, Z., Jiao, W., Shen, Y., Zhang, Y., Yang, Y., Liu, W., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02111 · Cited by 1
“Two-stage MIL: Two-stage MIL methods first segment WSIs into fixed-size patches using tools like CLAM [23] and extract features with pre-trained networks (e.g., ResNet [13], ViT [1], or other foundation models [5, 24, 38, 43]).”
InfluentialSong, J., Wang, J., Nguyen, A. T., Byeon, K., Ahn, S., Lee, S. H., & Kwak, J. T. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02049 · Cited by 1
“…descriptions, Biomed-CLIP [47] is trained on multimodal medical dataset covering microscopy, biosignals, scientific diagrams, and flowcharts, QuiltNet [15] is trained on 1M pathology image-text pairs, and CONCH [24] is based on CoCa and trained on a dataset with 1.17 million image-caption pairs.”
InfluentialHistopath-C: Towards Realistic Domain Shifts for Histopathology Vision-Language Adaptation
Noori, M., Hakim, G. A. V., Osowiechi, D., Shakeri, F., Bahri, A., Yazdanpanah, M., Dastani, S., Ayed, I. B., & Desrosiers, C. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00475 · Cited by 1
Bedau, T., Harder, C., Al-Shughri, A., Wang, Y., Pryalukhin, A., Eich, M., Lyu, S. I., Büttner, R., Quaas, A., & Tolkach, Y. (2026) · Communications Medicine · doi:10.1038/s43856-026-01601-x · Cited by 3
“6, 8, 9 Withthelarge, densely annotated training datasetsused forour models (>200 – 300 whole-slide images each with pixel-level annotations), the expected advantage of foundation encoders over ImageNet-pretrained encoders is likely diminished — though this remains an empirical question for…”
Yin, W., Peng, Q., Meng, F., Wan, Y., Zhang, W., & Zhou, Y. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00992-0 · Cited by 1
AI-driven pathology in esophageal cancer: from early screening to precision prognostics
Bian, Y., Li, J., Cao, J., Wang, S., & Ma, C. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1733548 · Cited by 1
“Consequently, LNM quanti fi - cation serves not only as a primary indicator for adjuvant therapy but also as a dynamic biomarker for adjusting the intensity of personalized treatment (68).”
Alasi, H., Yilmaz, I., Reynolds, J., Agarwal, S., Nassar, A., Zubair, A., Horbinski, C., Dangott, B. J., & Akkus, Z. (2026) · Bioengineering · doi:10.3390/bioengineering13050577 · Cited by 1
PathLens: A lightweight multimodal reasoner for in-depth pathology insights
Zhu, Z., Chen, H., Yan, Z., Zhang, D., Wu, Y., Zhan, Y., Cheng, W., Zhao, M., Gu, W., Chen, Y., et al. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116261 · Cited by 1
Zhong, J., Zhang, M., Liu, L., Liu, T., Jiang, J., Piao, Y., Xu, R., Tian, F., Sun, W., Bi, H., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104138 · Cited by 1
Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions
Zhang, B., Cui, V. Y., Wu, T., Su, G., Huang, Q., Shang, B., Kong, M., Yu, W., Yuan, Y., & Tu, H. (2026) · Research · doi:10.34133/research.1332 · Cited by 1
“As a result, reported performance may overestimate true generalization ability, particularly when benchmarks do not clearly exclude pretraining–evaluation overlap or provide transparent data provenance.”
InfluentialCai, L., Meng, B., Huang, J., Ding, G., Ju, M., Wang, W.-W., Shi-jie, D., Lai, L., Wang, J., Yang, C.-X., et al. (2026) · LabMed discovery. · doi:10.1016/j.lmd.2026.100130 · Cited by 1
Jiang, X., Li, P., Chen, Z., Yao, H., Jia, H., Wang, T., & Tang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1841261 · Cited by 1
“These technologies facilitate rapid slide-level analysis and can underpin large-scale, standardized evaluation of IBD biopsies (11, 13, 16, 17).”
Deep Learning Predicts Mutations and Outcomes in Gastrointestinal Stromal Tumors
Bonetti, A., Le, V.-L., Carrero, Z. I., Wolf, F. M., Gustav, M., Lam, S. W., Vanhersecke, L., Sobczuk, P., Loarer, F. L., Lenarcik, M., et al. (2026) · Cancer Research · doi:10.1158/0008-5472.can-26-0132 · Cited by 1
“WSIs were preprocessed with the STAMP pipeline (18) for tessellation and background rejection; patches were saved as JPEGs and subsequently used for feature extraction with CONCH (19) to train the downstream Vision Transformer (ViT) classification models.”
InfluentialData-Driven Vision-Language Models for Remote Sensing: A survey
Zhou, Y., Zhao, S., Yang, X., Li, R., Zhang, T., Lan, M., Chen, C., Ma, L., He, H., & Li, K. (2026) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2026.3696441 · Cited by 2
“This transformation implies that high-quality domain-specific data, rather than model architecture itself, has become the critical enabling factor for unlocking the potential of foundation models and addressing specialized tasks [19], [20].”
Tiwari, A. K., & Bhandari, A. K. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100404 · Cited by 2
MuGNet: a graph-based framework for multi-tissue integration in computational pathology
Miccolis, F., Demarco, M., Ficarra, E., & Lovino, M. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3820 · Cited by 1
Intelligent three-dimensional imaging in pathology: Applications and developments
Si, W., Zhao, F., Kuang, D., Zhao, Y., Liu, Y., Sun, M., Duan, Y. Q., Fei, P., & Wang, G. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004176 · Cited by 1
Retrieval-augmented large multimodal models for pathology
Chu, X., Sun, Y., Zhou, B., & Sun, L. J. (2026) · Discover Computing · doi:10.1007/s10791-026-10263-1 · Cited by 1
“Representative pathology-focused LMMs include Pmc-CLIP [28], BiomedCLIP [29], Quilt-Net [30], PLIP [31], PathCLIP [32], PathGen-CLIP [33], and CONCH [34].”
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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