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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]
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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]
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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]
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“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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Items 601–700 of 968
Huang, S., Liu, G., Cai, L., Cheng, H., Huang, Z., & Zhang, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3621757
“The features of all patches are then extracted using a ResNet-50 [19] pre-trained on ImageNet and the ViT image encoder from the pathology foundation model CONCH [20].”
Vashistha, R., Brosda, S., Belle, C. J., Aoude, L. G., Waddell, N., Ghosh, S., Cooper, C. L., Barbour, A. P., & Vegh, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.16.682972
“The proposed methodology integrates LLM, a pretrained visual encoder specifically trained on histopathological image tiles [10], and a human-in-the-loop feedback mechanism, effectively simulating expert-driven iterative refinement.”
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Zheng, B., Wu, A., Fan, K., Li, A., & Wang, M. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755012
“In particular, pioneering works like CONCH [33] and PLIP [22] have emerged as pathological foundation models, with the ability to improve diagnostic accuracy and streamline the clinical work-flow.”
Web engine for tumor pathology image retrievals on massive scalesPreprint
Zhong, Z., Huang, J., Li, X., Song, L., Gong, L., Ru, B., Paul, R., Levine, J., Zhang, Y., Aldape, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.25.684566
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Sharma, V., Alagha, A., Khellaf, A., Trinh, V. Q., & Hosseini, M. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-04339-9_17
Duran, J., Zou, Y., Vallières, M., & Enger, S. A. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100789
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He, K., Wei, D., & Ranger, B. J. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3636830
“Vision-language foundation models have demonstrated promising results in domains such as computational pathology [175], [176].”
Chen, Y., Chan, T. H., Chen, J., Li, L., Yin, G., & Yu, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3638243
“For a fair and complete comparison of di ff erent models, we use the same pretrained image encoder for feature extraction on all slides and evaluate all models on three widely used feature extractors, i.e., UNI2-H [45], KimiaNet [33] and CONCH [41].”
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Zhang, R., Fang, M., Liu, S., Wang, Z., Xie, X., Ma, X., Tian, J., & Dong, D. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3640398
“Recentvisual-textmodels demonstrate zero-shot learning capabilities [25], indicating that these models have acquired a certain level of semantic understanding of disease-related regions.”
Tabatabaee, S., & Khasawneh, M. T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5864720
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“Today, BCD techniques are essential in large-scale applications, such as developing foundation models for pathology and automating image diagnosis [4], [5].”
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“More specifically, we compare four feature extractor networks, namely (i) CTransPath TCGA LUSC Histology 0.5954 0.5239 0.5021 [18], (ii) CONCH [21] (iii) UNI [16], and (iv) HistoSSL [17].”
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Zafar, S. A., Qin, W., Chengliang, L., Khan, A. A., Nazir, A., Batool, H., Khalid, F., & Faisal, M. S. (2025) · medRxiv · doi:10.64898/2025.12.19.25342650
Golfe, A., Meseguer, P., Naranjo, V., & Colomer, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114423
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Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
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Chen, X., & Huang, J. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3765612.3767228
“We categorize the baselines into two groups: (1) non-finetuned models,including ResNet-50 [9], CLIP [22], PLIP [11]and CONCH [15]; (2)finetunedimage–genepretrainingmodels, including BLEEP [32], MCLSTExp [17] and STimage-1K4M [5].”
Zhang, Y., & Jiang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-26970-6
“On the other hand, synthetic data can help the model learn richer feature representations and reduce the risk of overfitting, thus improving the accuracy of recognition in complex scenes 30 .”
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Sun, J., Fei, H., Zhu, Z., Sun, Y. L., He, W., & Cai, H. (2025) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin64977.2025.11279496
“Patch-based processing has become a widely adopted strategy in large-scale pathology foundation models, such as UNI, CONCH, CHIEF, and TITAN [10,11,12,13], where gigapixel WSIs are divided into smaller tiles for feature extraction and disease classification [14].”
Yang, J., He, Q., Peng, J., Wang, Y., Li, J., Li, H., Liu, Y., Wang, Y., Hu, A., Ling, X., et al. (2026) · The Journal of Pathology Clinical Research · doi:10.1002/2056-4538.70113
“To validate the superiority of using Virchow2 as the instance-level encoder, we compared its performance with CONCH [28], PLIP [29], and ImageNet-pretrained ResNet [30,31].”
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Meseguer, P., del Amor, R., & Naranjo, V. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103964
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Xu, Y., Zhang, B., Zhu, W. J., Zhang, L., Liu, B., Zhang, J., & Zhang, Q. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356780
“As shown in Fig.1(b), these methods [6], [7], [14], [15] adopt a VLM-based framework using a dual-tower encoder (image encoder and text encoder), which is pre-trained on a large number of image-text pairs collected from the Internet or public educational resources [7].”
Zhang, Y., Fan, L., Tao, Z., Di, D., Song, Y., Liu, S., & Cong, C. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356519
“…leverage cross-stain information: To enhance patch-level feature extraction, a lightweight adapter placed after the H&E-pretrained encoder [10] is fine-tuned via Cross-Stain Patch-wise Alignment, leveraging the paired dataset to align H&E features with contextual cues from corresponding…”
InfluentialEnhancing Pathology Vision-Language Pre-Training: A Robust Baseline
Yang, C., Liu, Y., Hu, S., Liu, T., Ding, H., & Xi, S. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356606
“CONCH is pre-trained utilizing contrastive learning to enhance pathological image analysis tasks [26].”
MP-MIL: Multi-View Multiple Instance Learning with Positional Embedding to Predict PIK3CA Mutation
Li, G., Pan, L., Li, X., Luo, J., Liang, Q., & Peng, S. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357095
“Finally, image features for all extracted patches were computed using Gigapath [30] and CONCHv1 5 [31], a high-performance pathological feature extractor.”
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Wang, Y., Shi, J., Sun, D., Liu, B., Wu, Z., & Zheng, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356311
“All preprocessed inputs are encoded using Conch [17], a pre-trained histopathology vision-language foundation model.”
Pan, W., Lu, D., Yan, J., Xu, Z., Xiong, C., Wei, D., Wu, X., & Yuan, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356158
“To improve the discriminability of these embeddings, pathology foundation models [7], [8] have been developed, leveraging self-supervised learning to pretrain encoders on large-scale pathology datasets.”
He, Q., Ge, S., Huang, Q., Li, J., Guan, T., Wang, Z., & He, Y. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07109-0
SCULPT: Semantic-aware causal prompt tuning for out-of-distribution detection of whole slide images
Liang, D., Li, X., Liang, D., Liu, J., Zhu, Z., Li, X., Dong, S., Luo, G., Wang, W., Wang, K., et al. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115432
Liu, C., Cheng, Y., Li, T., Hao, Y., & Zhang, Q. (2026) · BMC Medical Imaging · doi:10.1186/s12880-025-02130-0
“…as these gigapixel digital representations of tissue specimens serve as the foundation of modern computational pathology, where precise identification of cellular and architectural patterns directly determines critical patient outcomes, treatment strategies, and prognostic evaluations [1–14].”
Bonetti, A., Le, V.-L., Carrero, Z. I., Wolf, F., Gustav, M., Lam, S. W., Vanhersecke, L., Sobczuk, P., Loarer, F. L., Lenarcik, M., et al. (2026) · medRxiv · doi:10.64898/2026.02.02.26345350
“CONCH-derived features and the clinical or molecular data served as input for ViT 23 classification model trained utilizing the default parameters as specified in the STAMP pipeline documentation.”
Video-Based Fall Risk Assessment Using Multimodal Large Language Models in Home Health Care
Gupta, P., Zhang, Z., Song, M., Michalowski, M., Hu, X., Štiglic, G., Peltonen, L. M., & Topaz, M. (2026) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001485
Zhang, M., Xia, Z., Liu, R., Qin, Z., Li, H., Xu, J., Long, Q., Shen, Y., Liu, B., & Liu, J. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1704217 · Cited by 1
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Qin, W., Liu-Swetz, Y., Tan, S., & Wang, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703870
“While recent progress in pathology foundation models (e.g., contrastive histology encoders 33 , TCv2 34 , UNI 35 , CONCH 36 , and GigaPath 37 ) provides powerful feature extractors trained at scale, our results highlight that expressive embeddings alone do not guarantee interpretability (or ex-15…”
Deep Learning-Based Screening for POLE mutations on Histopathology Slides in Endometrial CancerPreprint
van den Berg, N., Schoenpflug, L. A., Horeweg, N., Volinsky-Fremond, S., Wolf, J. B., Andani, S., Lafarge, M. W., Ørtoft, G., Jobsen, J. J., Razack, R., et al. (2026) · medRxiv · doi:10.64898/2026.02.06.26345335 · Cited by 1
HistoPAG‑Net: a multiscale position‑aware GNN for weakly supervised histopathology analysis
Rahman, T., Tarkhan, A., Chellappa, R., & Baras, A. S. (2026) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3087003
Clinical XAI For Classification in Pathology Images Via Vision-Language Model
Song, J., Lee, C., & 곽진태 (2025) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipw68931.2025.11386035
“Several VLMs have recently been developed through training on pathology-specific datasets [13, 14].”
Piao, Z. H., Wu, J., Ao, S., Chen, H., Chen, B., Du, X., Liu, B., & Li, L. (2026) · Research Square · doi:10.21203/rs.3.rs-8541267/v1
Self-citationmViSE: A visual search engine for analyzing multiplex IHC brain tissue images (spatial proteomics)
Huang, L., Mills, R., Mandula, S., Bai, L., Jeyhani, M., Redell, J. B., Nguyen, H., Prasad, S., Maric, D., & Roysam, B. (2026) · Scientific Reports · doi:10.1038/s41598-026-40620-5
A multimodal embedding model for sepsis data representation
Liu, T., Li, Y., Chen, H., Li, N., Zhang, Y., Huang, X., Wang, J., Chen, R., Zeng, Y., Liu, Y., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02446-3 · Cited by 1
“While many existing embedding models are primarily designed for image data 15 or image-text pairs 16,17 , SepsisDRMistailoredformulti-modaldatacomprisedofstructuredtabular data and textual information, which enhances representations by passing themthroughtwo differentdropoutlayers…”
Self-citationBeyond alignment: synergistic integration is required for multimodal cell foundation modelsPreprint
Richter, T., Zimmermann, E., Hall, J., Theis, F. J., Raghavan, S., Winter, P. S., Amini, A. P., & Crawford, L. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.23.707420 · Cited by 1
“…Learning of Representations (SimCLR) is a contrastive learning framework that maximizes agreement between different views using normalized temperature-scaled cross entropy loss (NT-Xent), where positive pairs are diagonal elements of the similarity matrix and negative pairs are off-diagonal.”
ADaFSlide: Adapting Vision-Language Foundation Model for Lifelong Whole Slide Image Analysis
Bui, D. C., Pham, L. H., Le, V. T. D., Hai, V. T., Tran, V. D., & Nakashima, Y. (2025) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc63995.2025.11394181
“In contrast, multi-979-8-3315-3251-2 modal models like CONCH [5] and TITAN [6] The logits for classification are produced by a linear classifier: where As the number of patches N i can be in the thousands, vanilla self-attention [18] may not scale well and can cause out-of-memory issues.”
Lohmann, J., Witte, A., Maier, A., Saak, C. C., Sauter, G., Zimmermann, M., Bonn, S., & Baumbach, J. (2026) · Array · doi:10.1016/j.array.2026.100725
Structuring pathology foundation models with domain knowledge
Brunekreef, J., & Teuwen, J. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.02.004
Cai, L., Jiang, S., Liang, J., Liu, F., Zhang, B., Reitsam, N. G., Zeng, Q., Hu, Z., Ma, Y., Li, Z., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.25.707931
Debsarkar, S. S., Aronow, B. J., & Prasath, V. B. S. (2026) · Neural Computing and Applications · doi:10.1007/s00521-025-11751-z
“● Evaluation of state-of-the-art VLMs (UNI [ 14 ], Prov-Gigapath [ 15 ], Conch [ 16 ], Llava [ 17 ], Llava-Next [ 18 ], Plip [ 19 ]) and their fusion in medical imaging for biomarker identification.”
TC-SSA: Token Compression via Semantic Slot Aggregation for Gigapixel Pathology Reasoning
(135585), Z. C., Yang, X., & Xu, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_58 · Cited by 12
Wang, R., Ba, W., Li, Y., Zhou, Y., Liu, B., Wang, B., Wang, Y., Yang, Z., Zhang, K., Yan, R., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_44
Hierarchical Tissue-Specific Modeling of Pathology Images Predicts Response in HER2+ Breast Cancer
Cui, W., Tan, T., Fan, M., & Li, L. (2026) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0554
Wang, C., Su, T.-S., & Lee, Y.-C. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102753
Tissue morphology predicts telomere shortening in human tissues
Yadav, A., Alvarez, K., Adeleye, A., Wang, Y. X., Jackson, M., & Sinha, S. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101336 · Cited by 2
Deep Generative Model of Macrophage Immune Response for Hepato-intestinal Tumor Therapy Optimization
Zhou, L., Tan, Y., Lv, W., Lin, K., Li, F., Ouyang, W., & Zhang, D. (2026) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0559
“Table 3 presents survival prediction performance, comparing GI-ImmunoWorld against Cox-PH, DeepSurv [ 39 ], SALMON [ 40 ], SurvPath [ 37 ], MCAT [ 34 ], PORPOISE [ 35 ], and CONCH [ 38 ].”
InfluentialBarrera, C., Jain, P., Corredor, G., Dam, T., Maurya, H., Pathak, T. B., Beshai, B., Perimbeti, S. P., Jagirdar, J. S., Higgins, K., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-025-01225-9
“Foundation model descriptions CONCH .”
InfluentialHigh-sensitivity pan-cancer AI assessment of lymph node metastasis via uncertainty quantification
Wang, X., Chen, Y., Liu, X., Qiu, C., Tang, H., Huang, T., Guo, S., Ma, S., Cai, M., Sun, Q., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02564-y
Han, C., Zhao, B., Deng, T., Huang, J., Liu, F., Lin, J., Lyu, S., Wang, L., Lu, C. Y., Liang, C., et al. (2026) · Cancer Research · doi:10.1158/0008-5472.can-25-3255 · Cited by 2
Zhang, S., Cai, Y., Chen, Y., Cai, X., & Li, P. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110084 · Cited by 1
The Impact of Cervical Cytology Category Imbalance on Self-Supervised Representation Learning
Liu, S., Chang, X., Bai, J., Chen, L., Zeng, S., Quan, T., Liu, X., & Cheng, S. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0048
“6 E) demonstrates a substantial advantage, especially when fine-tuned with only a D), on the other hand, evaluate linear probing and fine-tuning initialized with weights pre-trained on ImageNet-1K [14] and CONCH [21], where ImageNet-1K is shown in purple and CONCH is shown in blue.”
Xiao, Y., Dong, X., Quan, K., & Ma, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110170
HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction
Yuan, R., Zhang, Z., Wang, A., Hu, L., Hua, X., Peng, Y., Luo, J., & Yang, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_21
Ye, C., Lv, J., Li, G., Wu, R., Zeng, S., Jiang, N., Liu, B., Wu, J., Ji, D., & Zhang, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00672-z
“More recently, foundation model pretraining (Xiang et al. 2025; Lu et al. 2024; Huang et al. 2023) has emerged as a powerful advancement, learning transferable visual representations that serve as a strong base for various downstream tasks (Xu et al. 2024; Chen et al. 2024).”
Huang, Z., Xia, T., Wu, T., Liu, B., Huang, S., Bi, L., & Kim, J. (2026) · Scientific Data · doi:10.1038/s41597-026-07112-7
ResViTM-Net: Where local features meet global context, guided by patient priors for medical vision
He, S., Liao, S., Wu, Y., Wu, H., Zheng, Z., Wang, J., Wang, W., & Gao, M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110217
MorphoNet: Morphological sub-region-based structure learning for WSI analysis
Wang, F., Wu, F., Hu, M., He, J., Wang, L., Chen, J., Liang, L., Wang, S., & Yu, L. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104061
Shao, S., Shen, W., Han, C., Li, G., Wang, J. H., Zhang, X., Fang, Q., Wu, J., Luo, R., Liu, Y., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110215 · Cited by 1
MOOZY: A Patient-First Foundation Model for Computational Pathology
Kotp, Y., Trinh, V. Q., Pal, C., & Hosseini, M. S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37359-5_26 · Cited by 4
Kapadia, S., Flannery, B. T., & Viswanath, S. E. (2026) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3088121
Flannery, B. T., DeSilvio, T., & Viswanath, S. E. (2026) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3085615
Pathologist‑interpretable prediction of low grade glioma and glioblastoma from whole slide images
Obusan, M. B., Kapse, S., Miller, M. L., & Prasanna, P. (2026) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3087860
Lu, Y., Qi, M., Fu, Y., Xiao, Z., Shao, W., Tian, J., & Mu, W. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3682009
“…datasets and cutting-edge self-supervised learning (SSL), have enhanced patch embedding to capture distinct pathological characteristics [5], [9], [10], [11], [12], [13], [14], [15], the e ff ectiveness of downstream tasks ultimately hinges on the capacity of aggregation methods to integrate…”
Nguyen, H. N., Li, C., Peng, C., Simpson, P. T., Ye, N., & Nguyen, Q. H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.05.716435
“Recent models, including CONCH [10], CHIEF [21], and Virchow2 [24], leverage large-scale histopathology pretraining and incorporate weak supervision, multimodal alignment, and stain robustness across large-scale of whole slide images (WSIs) datasets, thereby improving anatomical coverage and…”
Habis, A. (2024) · theses.fr (ABES) · doi:10.70675/9f0ba848z7aa2z48d6za495z17276ffc8147
Breast cancer recurrence risk prediction based on MIL
Chen, J., & Xu, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1746041
Applications of Artificial Intelligence in Quantitative Imaging
Estépar, R. S. J., Curiale, A. H., & Nardelli, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-14103-3_27
Lu, R., Lin, A., Jiang, A., Feng, Y., Shen, J., Fang, X., Bai, Y., Peng, S., Zhang, J., Cheng, Q., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6534879
Farwa, U. E., Ali, S., & Kim, H. C. (2025) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet63943.2025.11472161
“CONCH [19] takes innovation to the next level by leveraging 1.17 million histopathology image-text pairs to empower pathologists to ask precise morphological questions with unparalleled accuracy.”
InfluentialZhang, R., Liu, S., Fang, M., Zhao, S., Guo, W., Wang, Z., Feng, X., Zhang, X.-Y., Xie, X., Tian, J., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113695
Zhang, S., Shi, Z., Siyao, D. U., Zhu, T., Li, P., Zhao, R., Zhu, X., Wu, S., Tan, Y., Xu, W., et al. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218508
Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics
Yang, Y., Wang, H., Li, E., Zheng, Y., Chen, Y., & Wu, X. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1799845
Conditional Random Fields for Interactive Refinement of Histopathological Predictions
Godelaine, T., Zanella, M., Khoury, K. E., Mahmoudi, S., Macq, B. M. M., & Vleeschouwer, C. D. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460329
QPath: A Question-Aware Multi-Resolution Vision Language Framework for Computational Pathology
Yang, S., Li, J., Ling, X., Chen, Y., Guan, T., & He, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463111
Wei, X., Han, M., Chen, Z., Hua, Z., Liu, Y., Ma, S., & Zhang, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463085
Quan, W., Zhu, Z., & Wang, L. S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463300
ReinPath: A Multimodal Reinforcement Learning Approach for Pathology
Zhou, K., Jiang, J., Zhang, Q., Zheng, S., Li, Q. P., & Xu, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463211
SLDPC: Slide-Level Dual-Prompt Collaboration for few-shot whole slide image classification
Yuan, L., Zheng, Y., Liu, W., Zhao, H., Zhang, W., Wei, B., & Chen, L. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102768
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Self-citationRevisiting Reconstruction-Based Anomaly Detection for Whole-Slide Image
Xiao, B., Wangulu, C., van der Kwast, T., Yousef, G. M., & Zabihollahy, F. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3687008
“With the CONCH [36] visual encoder, our model can outperform benchmark models for at least 20% regarding the AUROC, AP and F1-max.”
InfluentialMature tertiary lymphoid structures in pilonidal sinus disease
Gupta, R., Suresh, R. R., Roberson, J. L., Lee, M., Maguire, L. H., & Fang, V. (2026) · JID Innovations · doi:10.1016/j.xjidi.2026.100479
Pixelomics: The Omics-Style Interrogation of Whole Slide Images for Precision Pathology
Ullah, E., & Parwani, A. V. (2026) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000535
Self-citationXie, H., Xu, T., Yang, H., Lyu, M., Chen, C. Y., Tong, C., & Liu, S. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109407
Ramanathan, V., Xu, T., Pati, P., Ahmed, F., Goubran, M., & Martel, A. L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02217 · Cited by 3
“Here, we use the text encoder from CONCH [44], pre-trained on histology captions.”
Multimodal Fusion Architectures for Clinical Vision Language Models
Ghosh, S., & Raj, D. R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7315-7.ch003
AcZeroTS: Active Learning for Zero-Shot Tissue Segmentation in Pathology Images
Tang, J., Zhou, J., Qian, B., Wan, P., Zuo, Y., Shao, W., & Zhang, D. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02182
The Architecture, Capabilities, and Challenges of Vision-Language Models for Healthcare AI
Murali, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7315-7.ch006
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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