A clinical benchmark of public self-supervised pathology foundation models
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 res...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58796-1 |
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| author | 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 Chad Vanderbilt |
| author_facet | 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 Chad Vanderbilt |
| author_sort | Gabriele Campanella |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bf569c2256f3438ea529c413ecf4632a |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bf569c2256f3438ea529c413ecf4632a2025-08-20T03:18:53ZengNature PortfolioNature Communications2041-17232025-04-0116111210.1038/s41467-025-58796-1A clinical benchmark of public self-supervised pathology foundation modelsGabriele Campanella0Shengjia Chen1Manbir Singh2Ruchika Verma3Silke Muehlstedt4Jennifer Zeng5Aryeh Stock6Matt Croken7Brandon Veremis8Abdulkadir Elmas9Ivan Shujski10Noora Neittaanmäki11Kuan-lin Huang12Ricky Kwan13Jane Houldsworth14Adam J. Schoenfeld15Chad Vanderbilt16Windreich Department of AI and Human Health, Icahn School of Medicine at Mount SinaiWindreich Department of AI and Human Health, Icahn School of Medicine at Mount SinaiWindreich Department of AI and Human Health, Icahn School of Medicine at Mount SinaiWindreich Department of AI and Human Health, Icahn School of Medicine at Mount SinaiWindreich Department of AI and Human Health, Icahn School of Medicine at Mount SinaiDepartment of Pathology, Icahn School of Medicine at Mount SinaiDepartment of Pathology, Icahn School of Medicine at Mount SinaiDepartment of Pathology, Icahn School of Medicine at Mount SinaiDepartment of Pathology, Icahn School of Medicine at Mount SinaiDepartment of Genetics and Genomics, Icahn School of Medicine at Mount SinaiDepartment of Clinical Pathology, Sahlgrenska University HospitalDepartment of Clinical Pathology, Sahlgrenska University HospitalDepartment of Genetics and Genomics, Icahn School of Medicine at Mount SinaiDepartment of Pathology, Icahn School of Medicine at Mount SinaiDepartment of Pathology, Icahn School of Medicine at Mount SinaiDepartment of Medicine, Memorial Sloan Kettering Cancer CenterDepartment of Pathology, Memorial Sloan Kettering Cancer CenterAbstract 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.https://doi.org/10.1038/s41467-025-58796-1 |
| spellingShingle | 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 Chad Vanderbilt A clinical benchmark of public self-supervised pathology foundation models Nature Communications |
| title | A clinical benchmark of public self-supervised pathology foundation models |
| title_full | A clinical benchmark of public self-supervised pathology foundation models |
| title_fullStr | A clinical benchmark of public self-supervised pathology foundation models |
| title_full_unstemmed | A clinical benchmark of public self-supervised pathology foundation models |
| title_short | A clinical benchmark of public self-supervised pathology foundation models |
| title_sort | clinical benchmark of public self supervised pathology foundation models |
| url | https://doi.org/10.1038/s41467-025-58796-1 |
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