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Towards a general-purpose foundation model for computational pathology
Open accessAbstract
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology. Pretrained using over 100,000 diagnostic histopathological slides across 20 major tissue types, a self-supervised model is shown to outperform existing baselines across various clinically relevant computational pathology tasks.
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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 [9]
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
“we present Virchow, the largest foundation model for computational pathology to date.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
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
“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 [32]
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
“UNI [191], Prov-GigaPath [192], and Virchow [193] were pretrained solely on pathology image data to perform cancer classification, diagnosis, and prognosis.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [191]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Built on rich knowledge representations, these models have shown impressive performance across medical fields such as ophthalmology20, radiology21 and pathology22–25.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
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In the citing paper
“The vision module is based on the patch encoder CTransPath17 for the small and medium models, and UNI18 for the large model.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“We found out that the UNI foundation model32, when paired with multi-task AB-MIL, achieved the best predictive performance for the various TME signature prediction tasks.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [32]
In the citing paper
“In fact GMA is widely used to assess the performance of pathology foundation models23,25,26.”
From A clinical benchmark of public self-supervised pathology foundation models (2025) · cites this paper as [23]
In the citing paper
“Self-supervised learning has become a widely used approach for pretraining foundation models on thousands to tens of thousands of images without annotations or labels, marking an important paradigm shift in the field.20,21”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [20]
In the citing paper
“An increasing number of published studies show the potential of foundation models20,24; however, they are currently underrepresented in the field of liver diseases.4”
From Use of artificial intelligence for liver diseases: A survey from the EASL congress 2024 (2024) · cites this paper as [20]
In the citing paper
“These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“Despite the challenges in collecting large-scale datasets in the pathology domain, recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters35–42”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [41]
In the citing paper
“Specialty-specific foundation models20,21,23 typically focus on single imaging modalities”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
In the citing paper
“The scaling behavior observed in PanDerm’s performance aligns with recent foundation model trends20,22,23,59”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [23]
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“FMs are an emerging area of research in medical AI and have shown great potential as very flexible feature extractors for a variety of downstream tasks in domains such as histopathology [33].”
Fırıldak, K., Çelik, G., & Talu, M. F. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70161 · Cited by 3
“When the results of the proposed model on Kather MSI are examined, we can say that it exhibits higher success compared to other studies [39, 74] and a satisfactory general success in the two-class discrimination task.”
Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach
Amidi, E., Ramzanpour, M., Chen, M., Boucher, T., Varma, M., Samec, T., Lamon, B. D., Stransky, N., Miglarese, M. R., Oberley, M. J., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01037-x · Cited by 4
“Building on existing research in biomarker detection via deep learning 17,21,56 – 59 , our model leverages a large dataset of 33,014 cases, including a wide range of specimen types such as resections and biopsies.”
HASD: Hierarchical Adaption for Pathology Slide-Level Domain-Shift
Liu, J., Li, H., Chen, Y., Deutges, M., Sadafi, A., You, X., Breininger, K., Navab, N., & Schüffler, P. J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_32 · Cited by 3
Seyedshahi, F., Rakovic, K., Poulain, N., Quiros, A. C., Powley, I. R., Richards, C. J., Uraiby, H., Klebe, S., Moore, D. A., Nakas, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63846-9 · Cited by 2
“Recent self-supervised models, such as Hierarchical Image Pyramid Transformer (HIPT) 10 , CTransPath 11 , HistoSSLscaling 12 , UNI 13 , and Histomorphological Phenotype Learning(HPL) 14 as well as other self-supervised models such as RNAPath 15 , which focus on healthy tissue analysis, have been…”
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“While there is a range of datasets and methods in digital pathology [8,9,10,11], few of them are dedicated to skin and non-melanoma skin tumors.”
Jin, D., Shmatko, A., Patel, A., Rutz, S. P., Friedrich, L., Banan, R., Rahmanzade, R., Sievers, P., Hamelmann, S., Schrimpf, D., et al. (2026) · Nature Cancer · doi:10.1038/s43018-026-01186-3 · Cited by 6
“Hetairos was built using 6,115 slides from 4,961 patients with CNS tumors at the Department of Neuropathology, University Hospital pathology models 21–24 .”
Toward comprehensive cellular characterization of H&E slides
Adjadj, B., Bannier, P., Horent, G., Mandela, S., Klajer, G., Lyon, A., Schutte, K., Marteau, U., Gaury, V., Dumont, L., et al. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100696 · Cited by 3
“In this study, the evaluated foundation models were pretrained using these SSL approaches: UNI, UNI2, Virchow, and Virchow2G were trained using DINOv2, whereas Phikon and PhikonV2 were trained using iBOT and DINOv2, respectively.”
InfluentialPolitano, G. M. M., Benso, A., Rehman, H. U., & Re, A. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae112 · Cited by 3
“…approach features remarkable predictive performance ( 13 ,14 ) on various tasks relevant for computational biology such as shedding light on gene regulation ( 15 ) by predicting, among others, protein–nucleotide interfaces ( 16 ,17 ), chromatin accessibility ( 18 ) and phenotypes ( 19 ).”
BackgroundRetrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
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Disentangled Pseudo-Bag Augmentation for Whole Slide Image Multiple Instance Learning
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“…image analysis, various approaches have been proposed, such as leveraging large-scale data for pre-training foundational models [41], [42], [43], [44], improving the structure of MIL networks [24], [25], [26], [27], [37], and incorporating textual information for multimodal learning [45], [46],…”
Editorial for Special Issue on Foundation Models for Medical Image Analysis
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A Multimodal Vision-text AI Copilot for Brain Disease Diagnosis and Medical ImagingPreprint
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A Lung CT Foundation Model Facilitating Disease Diagnosis and Medical ImagingPreprint
Gao, Z., Zhang, G., Liang, H., Liu, J., Ma, L., Wang, T., Guo, Y., Chen, Y., Yan, Z., Chen, X., et al. (2025) · medRxiv · doi:10.1101/2025.01.13.25320295 · Cited by 3
TLPath predicts telomere length in human tissues from histopathology imagesPreprint
Yadav, A., Alvarez, K., Adeleye, A., Wang, Y. X., & Sinha, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.04.641489 · Cited by 3
“We analyzed the average similarity confidence scores of CONCH for all terms across all patches, grouped by UNI feature activation and telomere length.”
InfluentialBenchmarking pathology foundation models for non-neoplastic pathology in the placentaPreprint
Peng, Z., Ayad, M. A., Jing, Y., Chou, T., Cooper, L., & Goldstein, J. A. (2025) · medRxiv · doi:10.1101/2025.03.19.25324282 · Cited by 6
A fusocelular skin dataset with whole slide images for deep learning models
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“In parallel, we employed the LazySlide framework to segment tissue regions and tile each slide, then extracted high - dimensional image features from each tile using the deep learning vision model UNI 47,48 .”
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OpenSlideFM: A Computationally Efficient Multi-Scale Foundation Model for Computational PathologyPreprint
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“Each WSI was tiled into non-overlapping 256 x 256-pixel patches at 20× magnification, and for each patch a feature vector was extracted using the pre-trained foundation model UNI2-h [39], capturing broad morphological and tissue-architectural patterns.”
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“For each nucleus centroid, we extract one square image patch I of P = 224 pixels centered on the centroid, which matches the input size for UNI2 and CONCH, the pathology foundation models used in this study.”
InfluentialImage Analysis: Understanding and Mitigating Batch Effects in Histopathology
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“…be classified into three categories based on their relevance to pathology tasks, including backbones pre-trained on natural image datasets (such as ImageNet [29] or CIFAR-100 [30]), general-purpose models for pathology [31–33], and transfer learning models based on task-specific datasets [12, 14].”
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Deep learning for giant cell arteritis diagnosis on temporal artery biopsy
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“The development of models capable of automatically generating pathology reports can alleviate the burden on pathologists, enhance consistency in reporting, and improve overall efficiency [1], [2], [3].”
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Effective SMOTE boost with deep learning for IDC identification in whole-slide images
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“The classification of WSI improves significantly with models built upon Transformer architecture because they understand spatial patterns within WSIs along with temporal connections [43].”
From histology to diagnosis: Leveraging pathology foundation models for glioma classification
Saueressig, C. F., Delbridge, C., Scholz, D., Kazemi, A., Khan, M. Z., Metz, M., Meyer, B., Mitsdoerffer, M., Schüffler, P. J., & Wiestler, B. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110988 · Cited by 2
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Li, L., Shelton, L. E., Forest, T. W., Janardhan, K. S., Jenkins, T., Napolitano, M. J., Wang, R., Leigh, D. A., Shah, T., Carlson, G. E., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_64 · Cited by 2
CardiacCLIP: Video-Based CLIP Adaptation for LVEF Prediction in a Few-Shot Manner
Du, Y., Jiarong, G. U. O., & Li, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04971-1_5 · Cited by 2
Neural Proteomics Fields for Super-Resolved Spatial Proteomics Prediction
Zhao, B., Shi, W., Chao, H., Yang, Z., Zhang, Y., Song, M., & Jiang, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04984-1_37 · Cited by 2
Pathology Image Compression with Pre-trained Autoencoders
Yellapragada, S., Graikos, A., Triaridis, K., Li, Z., Nandi, T., Madduri, R., Prasanna, P., Saltz, J. H., & Samaras, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04937-7_42 · Cited by 2
Dang, T. M., Li, H., Guo, Y., Ma, H., Jiang, F., Miao, Y., Zhou, Q., Gao, J., & Huang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04927-8_22 · Cited by 2
Wang, H., Gao, P., Feng, S., & Ma, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf528 · Cited by 2
Yang, Y. T., Li, Y., Li, Y., Zhang, W., Lv, Y., Zhou, J., Li, Q., Chen, Q., & Shi, H. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c04868 · Cited by 2
Kurata, Y., Mimura, I., Kodera, S., Abé, H., Yamada, D., Kume, H., Ushiku, T., Tanaka, T., Takeda, N., & Nangaku, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-19297-9 · Cited by 2
“However, recently developed pathology foundation models trained on pathology slides with diverse color variations have demonstrated robust performance even without stain normalization 7,9,19,20 .”
Sun, X., Liu, J., Wu, L., Chen, X., Ma, X., Teng, F., Zhang, T., Su, H., Fan, X., Li, J., et al. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1687021 · Cited by 2
Evidential deep learning-based ALK-expression screening using H&E-stained histopathological images
Kosaraju, S., Parsa, S. P., Song, D. H., An, H. J., Choi, Y., Han, J., Yang, J. W., & Kang, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01981-9 · Cited by 2
“For theexperiment,weconsideredUNI2-hasapatch-encoder,whichisa recentlydevelopedgeneral-purposefoundationmodelforpathologyimage analysis 18 .”
MIPHEI-ViT: Multiplex immunofluorescence prediction from H&E images using ViT foundation models
Balezo, G., Trullo, R., Plà, A., Decencière, É., & Walter, T. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111564 · Cited by 7
“…including CNNs like ConvNeXt v2 Woo et al. [2023], Swin Transformer Liu et al. [2021], and plain ViTs Dosovitskiy et al. [2020], enabling integration of recent foundation models in histology, such as CTransPath, Univ2, and H-optimus-0 Wang et al. [2022], Chen et al. [2024], Saillard et al. [2024].”
Yu, Z., Zhang, S., Qiao, N., Zhao, Y., Yu, L., Peng, T., & Zhang, X. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103840 · Cited by 2
Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification
Guo, H., Zhang, Q., Gao, Z., Yang, S., Peng, S., Tao, X., Yu, T., Wang, Y., & Li, Q. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755262 · Cited by 2
“We compress the features extracted by UNI [5] and determine the 𝑘 -nearest neighbors of each patch based on their coordinate information, i.e. , selecting 𝑘 adjacent patches within a sliding window centered on a given patch.”
InfluentialLGPNet: A dual-branch parallel network for cervical cell image classification
Xin, Y., Li, Q., & Li, D. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109076 · Cited by 2
Wang, X., Zhou, Y., Barrera, C., Chen, Y. M., Song, B., Lu, C. Y., Yang, K., Koyfman, S. A., Lewis, J. S., Scahlper, K., et al. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.218134 · Cited by 2
Topology-aware cross-modal learning with dynamic gradient modulation for survival prediction
Yuan, Y., Li, G., Jiang, N., Lv, J., Ye, C., Ji, D., Ren, Y., Luo, G., & Zhang, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130492 · Cited by 2
Schuiveling, M., van Duin, I. A. J., ter Maat, L. S., van der Weerd, J. C., Verheijden, R. J., van den Berkmortel, F. W. P. J., Blank, C. U., Breimer, G. E., Burgers, F. H., Boers‐Sonderen, M. J., et al. (2026) · European Journal of Cancer · doi:10.1016/j.ejca.2026.116831 · Cited by 2
Shvetsov, N., Kilvaer, T. K., Tafavvoghi, M., Sildnes, A., Møllersen, K., Busund, L., & Bongo, L. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111326 · Cited by 4
“In practice, foundation models leverage enormous amounts of weakly or unlabeled data from millions of whole slide images (WSIs) and employ self-attention mechanisms to capture long-range dependencies and global context [11, 12, 13, 14].”
Artificial Intelligence and Machine Learning in Health Care
Liebovitz, D., Wu, A. Y., Reamer, C. B., & Vadasz, B. (2025) · Medical Clinics of North America · doi:10.1016/j.mcna.2025.07.011 · Cited by 2
Clinically aligned multi-modal image-text model for pan-cancer prognosis prediction
Lee, J., Leiby, J. S., Takemaru, L., Huang, Y., Noh, M., Kim, J., Lee, B., Lee, M. E., Oldridge, D. A., Eun, Y., et al. (2025) · BioData Mining · doi:10.1186/s13040-025-00495-0 · Cited by 2
“Since UNI [11] is an image-only encoder, we employed BioMedBERT [32] as the text encoder, whereas MUSK [3], being a vision–language model, uses MUSK-Language (MUSK-L) as its text encoder.”
InfluentialBiricz, A., Magyar, D., Gedda, B., Spanu, A., Fillinger, J., Pesti, A., Csabai, I., & Pollner, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-31646-2 · Cited by 2
“The pathology-pretrained UNI-ViT-L performed poorly (AP = 0.012 ± 0.003), reinforcing the need for domain-specific pretraining in ecological image analysis.”
InfluentialnnMIL: a generalizable multiple instance learning framework for computational pathology
Luo, X., Xiang, J., Ji, Y., & Li, R. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01767-8 · Cited by 3
DSEM: Gene pathway and histology dual-stage enhanced fusion model for survival prediction
Wang, M., Wang, N., Han, S., Xue, Y., & Zhang, C. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109288 · Cited by 2
Srinivasan, S., Ai, X., Lo, T. W. S., Gilson, A., Zou, M., Zou, K., Kim, H., Yang, M., Pushpanathan, K., Yew, S. M. E., et al. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.101050 · Cited by 4
Self‐Supervised Transfer Learning of Cross‐Domains Histopathological Images for Cancer Diagnosis
Zhu, J., Wang, Z., Wu, J., Li, C., Li, L., Shang, L., Wang, H., Tu, C., & Yin, J. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70278 · Cited by 3
Denoising spatial epigenomic data via deep matrix factorization
Wang, S. S., Xu, H., Wang, J., Xiao, Y., Dai, S., Lu, J., Cao, R., Chen, X., & Qu, K. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00941-3 · Cited by 2
“We evaluated multiple image feature extraction networks, including ResNet50 36 and pathology-specific foundation models 37,38 , and observed only marginal differences in model performance (Supplementary Fig.”
Self-citationComputer Vision-Based Medical Imaging Techniques: Past, Present, and Future
Lonchakov, A., Sinitca, A., & Kaplun, D. (2026) · IEEE Access · doi:10.1109/access.2026.3654393 · Cited by 4
Wang, C., Chu, T.-C., Wu, T.-K., Chung, Y.-P., Lin, S.-S., & Chao, T.-K. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02359-1 · Cited by 4
“…ef fi cient ensemble deep learning (DL) framework, namely Multilayered Attention-based Multiple Instance Learning Ensemble with Contrastive Transformer UNI 24 Feature Encoder (MAMILE-UNI), to detect malignancy in pleural effusion and ascites directly from cytology smear or cell block slides.”
InfluentialIntegrating artificial intelligence ( AI ) into colorectal cancer reporting
Bräutigam, K., Baker, A., Koelzer, V. H., Kather, J. N., & Graham, T. A. (2026) · The Journal of Pathology · doi:10.1002/path.70029 · Cited by 2
“Large-scale AI models trained on broad and diverse datasets that can be adapted to a wide range of downstream tasks with minimal fi ne-tuning, e.g. [4,5]. standard data elements with more novel biomarkers that can predict patient outcomes and treatment responses [77,78].”
LCM-Net: LLM-Driven Cross-Modality MoE Feature Fusion Network for Cancer Survival Analysis
Yang, S., Zhou, H., Yang, Y., Wang, W., Chen, S., Yang, G., Fu, H., & Zhu, L. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3657518 · Cited by 2
“To further evaluate the e ff ect of stronger pathology vision encoders, we replace ResNet-50 with UNI [71], a representative open-source pathology foundation model, in our network as well as in four state-of-the-art counterparts.”
Chen, S., Parreno-Centeno, M., Booker, G., Verghese, G. E., Mohamed, F. S., Arslan, S., Pandya, P., Oozeer, A., D’Angelo, M., Barrow‐McGee, R., et al. (2026) · npj Breast Cancer · doi:10.1038/s41523-026-00896-2 · Cited by 2
Multimodal dynamic fusion framework for survival prediction in clear cell renal cell carcinoma
Fu, B., Li, W., He, J., Xu, Z., Peng, Y., Liu, Z., Zhang, Y., Li, P., Huang, P., & Wang, R. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104217 · Cited by 2
Hasan, M. J., Ahmad, W. S. H. M. W., Fauzi, M. F. A., Abas, F. S., Cheah, P., Chiew, S.-F., & Looi, L. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00490-3 · Cited by 2
“More recently, foundation models trained on massive pathology datasets using self-supervised learning have emerged, offering robust and generalizable feature representations without extensive manual annotations (Chen et al. 2024; Xu et al. 2024; Filiot et al. 2023).”
InfluentialMcLoughlin, A., YinHo, H., Zhao, X., Hakansson, A. K., Moradi, A., Xu, Q. J., & Liu, Y. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00097 · Cited by 2
“The emergence of self-supervised learning frameworks [10, 43] has enabled practitioners to train patch embedding foundation models from large corpora of unlabeled WSIs and histology patches [9, 35, 52, 54].”
InfluentialZeng, G., Li, W., Mei, H., & Du, R. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1775750 · Cited by 2
“Pri Gle for primary Gleason Score, Sec Gle for Secondary Gleason Score, T, N, and M for tumor TNM stage system. tissue-level variations (14).”
Xia, Y., Zhou, J., Wang, M., Hu, T., Zhai, W., Zhang, Y., & Yu, Z. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102744 · Cited by 2
Advances in predicting omics profiles from imaging data
Beachum, A. H., Xiao, X., Zhou, Y., Li, Q. Y., Xiao, G., & Xu, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag090 · Cited by 2
“For example, SCHAF’s training integrates the H&E foundation model UNI [30], the scRNA-seq foundation model scGPT [52], and Tangram [53] for spatial alignment of scRNA-seq data.”
InfluentialTorchmil: A PyTorch-based library for deep multiple instance learning
Castro-Macías, F. M., Sáez-Maldonado, F. J., Morales-Álvarez, P., & Molina, R. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133286 · Cited by 2
Zheng, Q., Mei, H., Wang, D., Liu, X., Wang, L., & Chen, Z. (2026) · Annals of Surgical Oncology · doi:10.1245/s10434-026-19471-x · Cited by 2
Lee, K., Shin, H., Ohn, J., Park, J. H., Park, S., Park, I. K., Kang, C. H., Kim, Y. T., Choi, H., Koh, J., et al. (2026) · BMC Cancer · doi:10.1186/s12885-026-15885-8 · Cited by 2
Nateghi, R., Sun, A., Dang, H., Handa, N., Schnauss, M., Michael, J., Jang, J. W., Nezami, B. G., Saft, M., Neill, C., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01335-y · Cited by 2
Whole-body molecular and cellular mapping of the laboratory mouse
Clevenger, M. H., Cipurko, D., Patil, A., Li, B., Takahama, M., Mei, L., Plaster, M., Richey, G., Kawamoto, T., Bao, F. Y., et al. (2026) · Cell · doi:10.1016/j.cell.2026.03.006 · Cited by 4
Data- and knowledge-driven multimodal learning in computational pathology: A comprehensive survey
Liu, M., Cai, C., Chen, D., Li, J., Li, J., Ming, W., & Xu, J. (2026) · EngMedicine · doi:10.1016/j.engmed.2026.100132 · Cited by 2
Hou, W.-T., Pu, Z.-F., Xu, Z.-Y., Wu, A.-H., Liu, Z., Zhao, K., Duan, C.-L., Guo, J., Chen, K., Qiu, S.-Q., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104069 · Cited by 2
ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes
Anders, P., Sextro, M., Lingelbach, K., Standvoss, K., Pandhe, S., Ghosh, S., Böhm, C., Tietz, S., Krupar, R., Tharun, L., et al. (2026) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-25-4513 · Cited by 2
Zhang, H., Huang, Q., Shang, B., Su, G., & Tu, H. K. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-014429 · Cited by 2
He, Q., Tan, H., Xiao, B., Peng, X., Peng, C., Tan, Y., Liu, Y., Cao, Y., Lv, F. J., Zhao, W., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01415-z · Cited by 2
“To build MATCH-Net, a self-supervised pretrained network (UNI) was applied to extract patch-level microscopic features ( K MicroVision ) from the corresponding WSIs.”
InfluentialHe, Q., Li, J., Guan, T., Ma, Y., Zhao, Z., Wang, Y., Chen, H., Xu, Y., Ge, S., Zhang, Y., et al. (2026) · Advanced Science · doi:10.1002/advs.202520580 · Cited by 3
“Three general-purpose pathology foundation models were selected: UNI, PLIP, and CONCH [ 69–71 ].”
Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis
Fu, M., Ling, X., Chen, Y., Li, J., 付方磊, Yuan, H., Guan, T., He, Y., & Zhu, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464653 · Cited by 5
Zhang, P., Gao, C., Hua, K., Zhang, Z., & Li, S. (2026) · Nature Methods · doi:10.1038/s41592-026-03070-5 · Cited by 5
In context learning foundation models for materials property prediction with small datasets
Li, Q., Dong, R., Miklaucic, N., Hu, J., Omee, S. S., Wei, L., Dey, S., Hu, M., & Hu, J. (2026) · npj Computational Materials · doi:10.1038/s41524-026-02089-8 · Cited by 4
“This paradigm has reshaped diverse fi elds such as natural language processing 46 , computer vision 47 , medical imaging 48 , and chemistry 49 .”
Foundation models for time series forecasting and policy evaluation in infectious disease epidemics
Kalahasti, S., Faucher, B., Wang, B., Ascione, C., Carbajal, R., Enault, M., Vincent‐Cassy, C., Launay, T., Guerrisi, C., Boëlle, P., et al. (2026) · Epidemics · doi:10.1016/j.epidem.2026.100916 · Cited by 13
Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight
Gharaie, D., Feriz, A. M., & Safarpour, H. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag179 · Cited by 2
“However, these approaches remain constrained by their static snapshots as they capture tumor state at a single time point and provide l imited insight into dynamic processes such as therapeutic adaptation, metastatic dissemination, and microenvironmental reprogramming [8].”
Song, Y., Fan, J., Chang, H., & Cai, W. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00494 · Cited by 2
“Specifically, we utilize the UNI [6] model, pretrained using more than 100 million H&E-stained WSIs, for histopathology image feature extraction.”
Mallya, M., Mirabadi, A. K., Farahani, H., & Bashashati, A. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00331 · Cited by 2
“GPFM used UNI [7], CONCH [30] and Phikon [17], while AM-Radio used CLIP [38], DI-NOv2 [37], and SAM [26] as their teacher models.”
Metadata
- Authors
- Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., Williams, M., Oldenburg, L., Weishaupt, L. L., Wang, J. J., Vaidya, A., Le, L. P., Gerber, G., Sahai, S., Williams, W., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Mahmood, Faisal
- ORCID
- Venue
- Nature Medicine
- Published
- 2024-03
- Volume
- 30
- Issue
- 3
- Pages
- 850-862
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11403354
- OpenAlex
- W4392947521
- Semantic Scholar
- cac496ddebbff6bbba78242780cde01bd962bfc9
- Cited by
- 1,560
- References
- 170
- Citations by year
- 2026: 789 · 2025: 613 · 2024: 154
- FWCI
- 229.15
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- computational pathology, whole slide images, self-supervised learning, foundation models, unsupervised feature learning, tissue classification, few-shot learning, cancer subtyping, histopathology, transfer learning
- MeSH terms
- Artificial Intelligence, Workflow
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