ArticleNature communications2025
A clinical benchmark of public self-supervised pathology foundation models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
What it found
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Who cites it
70 citing papers in PubMed.
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- CellPrior-net: Prior-guided nuclei detection and classification for H&E whole-slide images.Journal of pathology informatics · 2026Article
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
- Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics.Journal of imaging informatics in medicine · 2026Review
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- PathScaleBench: A Multidimensional Benchmark of Cross-Scale Representation Behavior and Magnification-Shift Robustness in Pathology Foundation Models.Journal of imaging informatics in medicine · 2026Article
- Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning.JAAD international · 2026Article
- Leakage-Controlled Classification of Dataset-Derived Ordinal p53-Signature Size Categories in Fallopian-Tube Immunohistochemistry.Diagnostics (Basel, Switzerland) · 2026Article
- Histological aging signatures for monitoring tissue-specific aging and disease.Nature medicine · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Deep learning of pretreatment ascites cytopathology for platinum-resistance risk stratification in advanced epithelial ovarian cancer.Neoplasia (New York, N.Y.) · 2026Article
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation.Biomedicines · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
- Domain Generalization of Histopathology Foundation Models in Multicenter, Multi-Scanner Cohorts: A Comparative Benchmark.Journal of imaging · 2026Article
- Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model.Research square · 2026Article
- REMIL-IBD: Region-filtered multiple instance learning for interpretable slide-level grading of inflammatory bowel disease.Journal of pathology informatics · 2026Article
- AI-based tumor cellularity assessment in digital pathology: A review of methods, datasets, and clinical translation.Journal of pathology informatics · 2026Review
- Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data.Nature biomedical engineering · 2026Article
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
The use of self-supervised learning to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. In this work, we present a collection of pathology datasets comprising clinical slides associated with clinically relevant endpoints including cancer diagnoses and a variety of biomarkers generated during standard hospital operation from three medical centers. We leverage these datasets to systematically assess the performance of public pathology foundation models and provide insights into best practices for training foundation models and selecting appropriate pretrained models. To enable the community to evaluate their models on our clinical datasets, we make available an automated benchmarking pipeline for external use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.