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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]
The cited source says what the claim attributes to it.
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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Items 301–400 of 1,003
Classification of Whole-Slide Pathology Images Based on State Space Models and Graph Neural Networks
Ding, F., Cai, C., Li, J., Liu, M., Jiao, Y., Wu, Z., & Xu, J. (2025) · Electronics · doi:10.3390/electronics14102056 · Cited by 6
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“In our analysis, we maintain the remaining components of the framework (Transformer + Sparse + K-means) and evaluate six patch embedding methods:CTransPath(768D) 54 ,DINO 32 (384D),DINO 20 (384D),DINO 1 K (384D), DINO 8 K (384D), and UNI (1024D) 56 .”
InfluentialMammadov, A., Folgoc, L. L., Adam, J., Buronfosse, A., Hayem, G., Hocquet, G., & Gori, P. (2025) · Journal of medical imaging · doi:10.1117/1.jmi.12.6.061404 · Cited by 7
“Table 4 reports MIL methods’ performance when using foundation models as feature extractors, including the general-purpose DINOv2 model [62] and in-domain CTransPath [44], PathAugFM [52] and UNI [64].”
Xia, K., Hu, Y., Cai, S., Lin, M., Lu, M., Lu, H., Ye, Y., Lin, F., Gao, L., Xia, Q., et al. (2025) · Patterns · doi:10.1016/j.patter.2025.101286 · Cited by 11
Self-citationAn end-to-end multifunctional AI platform for intraoperative diagnosis
Zheng, X., Zheng, K., Wen, Y., Meng, J., Zhang, X., Wen, X., Zhao, Z., Zheng, C., Cai, X., Lin, J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01808-7 · Cited by 7
“As the foundation model, UNI 21 , was pretrained on over 100 million pathology images using the MoCoV3 41 framework and already captures extensive domain knowledge, the adapter strategy preserves core representations while introducing task-speci fi c features for quality control, thereby improving…”
InfluentialDynamic hypergraph representation for bone metastasis analysis
Chen, Y., Li, J., Zhu, L., Yang, X., Guan, T., Shi, H., Yonghong, H., & Han, A. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108966 · Cited by 7
Virtual Histological Staining as a Tool for Extending Renal Segmentation Across Stains
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“Alternatively, deep learning can be utilized by treating the pathological slides as images 8,9 , segmenting them into multiple small patches, and using pixel-based visual methods for analysis 10 – 14 .”
StaDis: Stability distance to detecting out-of-distribution data in computational pathology
Zhang, D., Ge, J., Liu, J., Wang, C., Gong, T., Gao, Z., & Li, C. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103774 · Cited by 7
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“21–24 Our aim was to develop DL models to predict poor outcome-associated features, by capturing molecular subtypes, as well as to directly predict mVI by using one single routine hematoxylin and eosin (H&E)-stained pathology slide.”
Zhang, Y., Li, H., Sun, Y., Shui, Z., Li, J., Zhu, C., & Yang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_5 · Cited by 13
“A preliminary comparison is shown in Table 1.”
Han, M., Yang, D., Cheng, J., Zhang, X., Chen, Z., Kuang, H., & Zhang, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112458 · Cited by 11
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“For the transformer pipeline, tiles were embedded within the STAMP framework using frozen (weights kept constant) encoders/feature extractors (ViT-B/16 38 , CTransPath 39 , Prov-GigaPath 40 , H-Optimus-0 41 , UNI-2 42 , Virchow2 43 ) which were downloaded through https:// huggingface.co/.”
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Self-citationSMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning
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Foundation models in clinical oncology: Progresses and perspectives
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Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction
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“To demonstrate the frame-work’s generalizability, we employ multiple feature extractors (ResNet-50 [25], PLIP [26], UNI [27]) and baseline models (AB-MIL [14], TransMIL [9], DSMIL [15]).”
Yu, Z., Xia, Z., Xu, D., Zhang, Z., Zhang, L., Zhang, P., Wu, L., Wang, B., Wang, H., & Zhao, Z. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02289-4 · Cited by 7
“RecentfoundationmodelslikeCHIEF 4 , Virchow 13 , andUNI 28 leverage large-scale pretraining on tens of thousands to millions of whole-slide images to achieve strong performance in cancer detection and grading.”
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3D foundation model for generalizable disease detection in head computed tomography
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“DINO 7,24 is a self-supervised learning method shown promising and robust downstream evaluation performance in previous studies on different areas 12,13 .”
InfluentialPan-Cancer Histopathology WSI Pre-Training With Position-Aware Masked Autoencoder
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“Most of the current large-scale pathology foundation models focused on patch-level representation learning [20], [21], [22], [41], [54].”
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“29 UNI is currently the largest pretrained visual encoder for histopathology (trained on 100 million images and 100,000 WSIs), developed specifically for internal tumors, infections, inflammation, and normal tissues.”
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“These models are capable of accomplishing diverse computational pathology tasks, including tumor subtyping and outcome prediction, without the need for precise region or pixel-level annotations (25, 45 – 47).”
Abnormality-aware multimodal learning for WSI classification
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“UNI (30) uses self-supervised learning to extract features from unlabeled data, significantly improving prediction accuracy and reducing reliance on labeled data.”
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ProGIS: Prototype-Guided Interactive Segmentation for Pathological Images
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“Large-scale pre-trained backbone networks, namely CONCH [34] and UNI [35], were employed for experimental evaluation.”
Histological aging signatures enable tissue-specific disease prediction from bloodPreprint
Abila, E., Buljan, I., Zheng, Y., Veres, T., Weng, Z., Nackenhorst, M. C., Hulla, W., Tolkach, Y., Wöehrer, A., & Rendeiro, A. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.14.618081 · Cited by 8
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Self-citationTracing colorectal malignancy transformation from cell to tissue scalePreprint
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Self-citationComprehensive Benchmark Dataset for Pathological Lymph Node Metastasis in Breast Cancer Sections
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He, Q., Xu, Y., Huang, Q., Li, J., He, Y., Wang, Z., & Guan, T. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102495 · Cited by 5
Deep learning for fetal inflammatory response diagnosis in the umbilical cord
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Xia, P., Chen, D., An, H., Lim, K. S., & Yang, X. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103652 · Cited by 5
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“Furthermore, the use of pre-trained models, which are widely applied in the fi eld of pathology, (Chen et al., 2024; Vorontsov et al., 2023; Ding et al., 2024; Xu et al., 2024) has signi fi cantly enhanced the capability of models to extract morphological features.”
Høibø, M., Spiske, U., Pedersen, A., Ytterhus, B., Akslen, L. A., Wik, E., Askeland, C., Reinertsen, I., Smistad, E., & Valla, M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1593143 · Cited by 5
“The aim of this study was to predict ER status in HE-stained breast cancer TMAs using CLAM (27) with the vision-encoder UNI (42).”
InfluentialCai, C., Shi, Q., Liu, M., Li, J., Zhou, Y., Xu, A., Zhang, D., Jiao, Y., Liu, Y., Cui, X., et al. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106024 · Cited by 5
Liang, M., Zhang, S., Wang, X., Li, B., Javed, M. H., Jia, X., & Wang, L. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3583127 · Cited by 6
“To further demonstrate the robustness of NSB-H 2 GAN, we replaced the default ResNet-50 backbone with UNI [49], a SOTA domain-specific feature extractor in computational pathology.”
Grisi, C., Kartasalo, K., Eklund, M., Egevad, L., van der Laak, J. A. W. M., & Litjens, G. J. S. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103663 · Cited by 5
Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy
Hu, X., Yang, J., Li, Y., Gong, Y., Ni, H.-F., Wei, Q., Yang, M., Zhang, Y., Huang, J., Ma, C., et al. (2025) · Renal Failure · doi:10.1080/0886022x.2025.2528106 · Cited by 5
“Recent studies have highlighted the significant potential of ‘mul-timodal’ and ‘large model’ technologies in digital pathology [35,36]. which underscores the pressing need to integrate multimodal research methodologies into renal disease studies. dl has exhibited extensive applicability in the…”
Khan, H., Su, Z., Zhang, H., Wang, Y., Ning, B., Wei, S., Guo, H., Li, Z., & Niazi, M. K. K. (2025) · Cancers · doi:10.3390/cancers17152423 · Cited by 5
“…self-supervised pathology foundation model trained on 1.2 million histopathology slides) [46] to extract discriminative feature h i ∈ R d for each patch i , where d = 1536 denotes the feature dimensionality, corresponding to the output of the penultimate layer of the UNI v2 encoder [46,47].”
InfluentialFlexible and scalable federated learning with deep feature prompts for digital pathology
Cong, C., Song, Y., Ieva, A. D., Chou, A. S. Y., Gill, A. J., Coiera, E. W., & Liu, S. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02710-6 · Cited by 5
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HyperPath: Knowledge-Guided Hyperbolic Semantic Hierarchy Modeling for WSI Analysis
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Zhao, H., Tao, D., Zhan, Y., Ni, J. F., & Chen, Y. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3620443 · Cited by 6
Histolytics: A panoptic spatial analysis framework for interpretable histopathology
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“More recently, the advent of deep learning (DL) approaches has introduced a powerful predictive modeling paradigm which has led to significant increases in computational prognostic accuracy, automated grading of tumors, and prediction of treatment response [1 – 5].”
Novel concepts of cell-of origin in neuroendocrine neoplasms
Marinoni, I., Avanthay, S., & Alcala, N. (2025) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-025-04311-2 · Cited by 5
Matsui, I., Matsumoto, A., Imai, A., Okushima, H., Niioka, H., Abe, M., Tamai, N., Nagasu, H., Kanda, E., Uchino, E., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02160-6 · Cited by 5
“Additionally, benchmark comparison with a pathology foundation model revealed that our standard YOLOv8 approach consistently outperformed UNI-based models across evaluation scenarios (Supplementary Table 7) 25 .”
InfluentialPredicting lymph node metastasis in colorectal cancer using case-level multiple instance learning
Zou, L., Wang, X.-B., Li, J.-W., Ouyang, X., Luo, Y.-Y., Luo, Y., & Wang, C.-L. (2026) · World Journal of Gastroenterology · doi:10.3748/wjg.v32.i1.112090 · Cited by 6
“5, followed by normalisation using ImageNet parameters [mean: (0.485, 0.456, 0.406); SD: (0.229, 0.224, 0.225)][20,21].”
InfluentialZhang, K., Dong, S., Shi, P., Hu, D., Gao, G., Yang, J., Gan, T., & Rao, N. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102699 · Cited by 6
Waqas, M., Bandyopadhyay, R., Showkatian, E., Muneer, A., Zafar, A., Alvarez, F. R., Marin, M. C., Li, W., Jaffray, D., Haymaker, C., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01312-5 · Cited by 11
Efficient Self-Supervised Grading of Prostate Cancer Pathology
Bhattacharyya, R., Pal, S., & Mitra, S. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3662748 · Cited by 6
“Comparison was also made with histopathology-specific foundation models like CtransPath [32], PathoDuet [45], Kimi-anet [31], CHIEF [46], GigaPath [47], UNI2 [33], and Virchow [34], on the same three metrics.”
From modality-specific to compositional foundation models for cell biology
Bahrami, M., Richter, T., Schmacke, N. A., Lavandera, A. E., & Theis, F. J. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101534 · Cited by 7
Ahmad, F., Jaffar, A., Latif, G., Alghazo, J. M., & Bhatti, S. M. (2026) · Diagnostics · doi:10.3390/diagnostics16050653 · Cited by 5
Pathology Public Datasets for Artificial Intelligence: A Systematic Review
Chinnachinnanagari, A. R., Debsarkar, S. S., & Prasath, V. B. S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01899-y · Cited by 7
Tackling the complexity of cancer with generative models
Conard, A. M., Hughes, M., Hall, J., Tenenholtz, N., Zimmermann, E., Crawford, L. A., Amini, A. P., & Severson, K. (2026) · Cell · doi:10.1016/j.cell.2026.03.027 · Cited by 5
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Computational pathology for breast cancer: Where do we stand for prognostic applications?
Gessain, G., & Lacroix‐Triki, M. (2025) · The Breast · doi:10.1016/j.breast.2025.104464 · Cited by 5
Nguyen, M. D., Nguyen, D. T., Nguyen, T. V., Yamada, H., Pham, H., & Nguyen, P. L. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980711 · Cited by 6
“Hence, we propose to adopt Low-rank Adaptation (LoRA) [12] to finetune ViT, i.e., UNI [13] in particular.”
Use of Large Language Models in Clinical Cancer Research
Kehl, K. L. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00027 · Cited by 9
Tampu, I. E., Nyman, P. O., Spyretos, C., Blystad, I., Shamikh, A., Prochazka, G., de Ståhl, T. D., Sandgren, J., Lundberg, P., & Haj‐Hosseini, N. (2025) · Brain Pathology · doi:10.1111/bpa.70029 · Cited by 7
“Among the domain generalization methods, self-supervised pre-training of feature encoders on a large and diverse dataset has been shown to improve the generalization of learned representations [17].”
InfluentialZhong, L., Huang, Z., Liu, Y., Liao, W., Zhang, S., Wang, G., & Zhang, S. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3595111 · Cited by 13
“Restrictions apply.”
InfluentialLazySlide: accessible and interoperable whole-slide image analysis
Zheng, Y., Abila, E., Chrenková, E., Buljan, I., Winkler, J., & Rendeiro, A. F. (2026) · Nature Methods · doi:10.1038/s41592-026-03044-7 · Cited by 10
Detecting Noisy Labels with Repeated Cross-Validations
Chen, J., Ramanathan, V., Xu, T., & Martel, A. L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_19 · Cited by 6
Pocevičiūtė, M., Ding, Y., Bromée, R., & Eilertsen, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109327 · Cited by 6
Foundation Models for Translational Cancer Biology
Tsang, K. K., Kivelson, S., Cortina, J. M. A., Kuchi, A., Berkowitz, J. S., Liu, H., Srinivasan, A., Friedrich, N. A., Fatapour, Y., & Tatonetti, N. P. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095633 · Cited by 5
Harnessing transcriptional regulation of alternative end-joining to predict cancer treatment
Espín, R., Medina‐Jover, F., Sigüenza-Andrade, J., Farran-Matas, S., Mateo, F., Figueras, A., Sanz, R. T., Vicent, G. P., Shabbir, A., Ruiz-Auladell, L., et al. (2025) · NAR Cancer · doi:10.1093/narcan/zcaf007 · Cited by 7
“Next, we applied a deep-learning method [ 79 , 80 ] to predict alt-EJ and hypoxia status, as well as common gene drivers, using tumor pathology images from TCGA BRCA [ 61 ], OV [ 123 ], and PRAD [ 124 ] cancers.”
Xu, J., Shi, L., Li, S., Zhang, Y., Zhao, G., Shi, Y., Li, J., & Gao, Y. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3565184 · Cited by 5
“UNI [16] breaks through the limitations of pre-training data size and diversity.”
InfluentialDual-masked contrastive learning based hypergraph foundation model for whole slide images
Zhou, X., Ding, S., Zhang, W., Li, J., Wang, J., Chen, J., & Shi, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111995 · Cited by 5
Self-citationDeep Learning-based Modeling for Preclinical Drug Safety AssessmentPreprint
Jaume, G., de Brot, S., Song, A. H., Williamson, D. F. K., Oldenburg, L., Zhang, A., Chen, R. J., Asín, J., Blatter, S., Dettwiler, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.20.604430 · Cited by 5
Self-citationExplainable AI for computational pathology identifies model limitations and tissue biomarkersPreprint
Kaczmarzyk, J., Kim, C., Gadgil, S. U., Savant, D., Zhao, Z., Saltz, J. H., Lee, S., & Koo, P. K. (2024) · PubMed · doi:10.48550/arxiv.2409.03080 · Cited by 18
“We standardized hyperparameters across all ABMIL models, adapting from Chen et al. 58 .”
InfluentialUnifying the Electron Microscopy Multiverse through a Large-scale Foundation ModelPreprint
He, L., Shi, R., Wang, W., Fang, G., Cai, Y., & Ма, Л. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.13.648639 · Cited by 5
“In contrast, most existing self-supervised frameworks operate in a single data dimensionality: many are strictly 2D, as in natural images 43,48 , whole-slide imaging 49 , and EM-specific methods 13,47 , whereas others are fully 3D for biomedical image pretraining 46,50 .”
A large-scale curated and filterable dataset for cryo-EM foundation model pre-training
Chen, Q., Xu, Z., Dai, H., Shen, Y., Zhang, J., Liu, Z., Pei, Y., & Jingyi Yu, A. (2025) · Scientific Data · doi:10.1038/s41597-025-05179-2 · Cited by 7
Jahanifar, M., Dawood, M., Tajeddin, N. Z., Shephard, A., Chohan, B. S., Bertram, C., Wahab, N., Eastwood, M., Aubreville, M., Raza, S. E. A., et al. (2025) · medRxiv · doi:10.1101/2025.06.07.25329181 · Cited by 5
Polyploid cardiomyocytes define disease-specific transcriptional states in the mammalian heartPreprint
Kießling, P., Joodaki, M., Pikulska, D., Scheidereit, E., Cesaro, G., Ruiz, M., Kühn, D., Peng, K., Goni, O., Foecking, S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.31.701472 · Cited by 5
SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mappingPreprint
Yates, J., Shavakhi, M., Choueiri, T. K., Allen, E. M. V., & Uhler, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.16.712056 · Cited by 5
Shamai, G., Cohen, S., Binenbaum, Y., Sabo, E., Cretu, A., Mayer, C., Barshack, I., Goldman, T., Bar-Sela, G., Polónia, A., et al. (2025) · doi:10.1101/2025.05.15.25327686 · Cited by 3
Polack, M., van Pelt, G. W., van den Heuvel, D. H., Kranenbarg, E. M., Roodvoets, A. G. H., Putter, H., Crobach, A. S. L. P., Nagtegaal, I. D., Peeters, K. C. M. J., Tollenaar, R. A. �. �., et al. (2025) · Histopathology · doi:10.1111/his.15423 · Cited by 5
“61 Future studies could use this collection for developing algorithms, for example, supervised deep-learning models trained by pathologists’ annotations to characterise tissue types, 62 resulting in auto-matised TSR quantification in biopsies, thus supporting the pathologists’ workload.”
Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
Lv, J., Zhu, Y., Tenorio, C. G. C., Chohan, B. S., Eastwood, M., & Raza, S. E. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_5 · Cited by 4
Chai, X., Li, H., Yang, M., Zeng, J., Chen, G., Li, Y., Wang, W., Liu, Z., Li, K., Zhang, T., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06956-8 · Cited by 5
“We initially applied the UNI [32] model to a survival prediction task.”
A position-aware sets based weakly supervised framework for whole-slide subtype classification
Song, J., Yu, B., Liu, X., Cong, L., Zhou, Z., Cong, X., Sun, H., Pang, S., & Chen, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111998 · Cited by 4
Prototype-Guided Cross-Modal Knowledge Enhancement for Adaptive Survival Prediction
Liu, F., Cai, L., Wang, Z., Fan, Z., Yu, J.-G., Chen, H., & Zhang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_50 · Cited by 6
PTCMIL: Multiple Instance Learning via Prompt Token Clustering for Whole Slide Image Analysis
Zhao, B., Kim, S., Chen, H., Zhou, C., Gao, Z., Wang, G., & Li, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05182-0_49 · Cited by 4
Shi, P., Chen, J., & Secrier, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_57 · Cited by 5
Top-Down Attention-Based Multiple Instance Learning for Whole Slide Image Analysis
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Using spatial proteomics to enhance cell type assignments in histology images
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Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning
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Explainable Artificial Intelligence in Echocardiography
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“UNI is a universal pathological self-supervised model pre-trained on over 100,000 diagnostic H&E-stained WSIs (more than 77 TB of data), encompassing 20 major tissue types and over100millionimages 27 .”
Luo, H., Huang, J., Zhou, T., Wu, E., & Ding, W. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3609687 · Cited by 5
“Moreover, recent pathology-specific models like PLIP [45], CONCH [46], UNI [47], and mSTAR [48] have demonstrated superior feature extraction capabilities compared to ResNet50 pretrained on ImageNet.”
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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