A clinical benchmark of public self-supervised pathology foundation models

Gabriele Campanella (), Shengjia Chen, Manbir Singh, Ruchika Verma, Silke Muehlstedt, Jennifer Zeng, Aryeh Stock, Matt Croken, Brandon Veremis, Abdulkadir Elmas, Ivan Shujski, Noora Neittaanmäki, Kuan-lin Huang, Ricky Kwan, Jane Houldsworth, Adam J. Schoenfeld and Chad Vanderbilt ()
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Gabriele Campanella: Icahn School of Medicine at Mount Sinai
Shengjia Chen: Icahn School of Medicine at Mount Sinai
Manbir Singh: Icahn School of Medicine at Mount Sinai
Ruchika Verma: Icahn School of Medicine at Mount Sinai
Silke Muehlstedt: Icahn School of Medicine at Mount Sinai
Jennifer Zeng: Icahn School of Medicine at Mount Sinai
Aryeh Stock: Icahn School of Medicine at Mount Sinai
Matt Croken: Icahn School of Medicine at Mount Sinai
Brandon Veremis: Icahn School of Medicine at Mount Sinai
Abdulkadir Elmas: Icahn School of Medicine at Mount Sinai
Ivan Shujski: Sahlgrenska University Hospital
Noora Neittaanmäki: Sahlgrenska University Hospital
Kuan-lin Huang: Icahn School of Medicine at Mount Sinai
Ricky Kwan: Icahn School of Medicine at Mount Sinai
Jane Houldsworth: Icahn School of Medicine at Mount Sinai
Adam J. Schoenfeld: Memorial Sloan Kettering Cancer Center
Chad Vanderbilt: Memorial Sloan Kettering Cancer Center

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58796-1

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