PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908380322332672 |
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| author | Ma, Jiabo Xu, Yingxue Zhou, Fengtao Wang, Yihui Jin, Cheng Guo, Zhengrui Wu, Jianfeng Tang, On Ki Zhou, Huajun Wang, Xi Luo, Luyang Zhang, Zhengyu Cai, Du Gao, Zizhao Wang, Wei Liu, Yueping He, Jiankun Cui, Jing Li, Zhenhui Zhang, Jing Gao, Feng Zhang, Xiuming Liang, Li Chan, Ronald Cheong Kin Wang, Zhe Chen, Hao |
| author_facet | Ma, Jiabo Xu, Yingxue Zhou, Fengtao Wang, Yihui Jin, Cheng Guo, Zhengrui Wu, Jianfeng Tang, On Ki Zhou, Huajun Wang, Xi Luo, Luyang Zhang, Zhengyu Cai, Du Gao, Zizhao Wang, Wei Liu, Yueping He, Jiankun Cui, Jing Li, Zhenhui Zhang, Jing Gao, Feng Zhang, Xiuming Liang, Li Chan, Ronald Cheong Kin Wang, Zhe Chen, Hao |
| contents | The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer diagnosis, and prognosis assessment. While these models show remarkable potential across cancer diagnostics and prognostics, their clinical translation faces critical challenges including variability in optimal model across cancer types, potential data leakage in evaluation, and lack of standardized benchmarks. Without rigorous, unbiased evaluation, even the most advanced PFMs risk remaining confined to research settings, delaying their life-saving applications. Existing benchmarking efforts remain limited by narrow cancer-type focus, potential pretraining data overlaps, or incomplete task coverage. We present PathBench, the first comprehensive benchmark addressing these gaps through: multi-center in-hourse datasets spanning common cancers with rigorous leakage prevention, evaluation across the full clinical spectrum from diagnosis to prognosis, and an automated leaderboard system for continuous model assessment. Our framework incorporates large-scale data, enabling objective comparison of PFMs while reflecting real-world clinical complexity. All evaluation data comes from private medical providers, with strict exclusion of any pretraining usage to avoid data leakage risks. We have collected 15,888 WSIs from 8,549 patients across 10 hospitals, encompassing over 64 diagnosis and prognosis tasks. Currently, our evaluation of 19 PFMs shows that Virchow2 and H-Optimus-1 are the most effective models overall. This work provides researchers with a robust platform for model development and offers clinicians actionable insights into PFM performance across diverse clinical scenarios, ultimately accelerating the translation of these transformative technologies into routine pathology practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology Ma, Jiabo Xu, Yingxue Zhou, Fengtao Wang, Yihui Jin, Cheng Guo, Zhengrui Wu, Jianfeng Tang, On Ki Zhou, Huajun Wang, Xi Luo, Luyang Zhang, Zhengyu Cai, Du Gao, Zizhao Wang, Wei Liu, Yueping He, Jiankun Cui, Jing Li, Zhenhui Zhang, Jing Gao, Feng Zhang, Xiuming Liang, Li Chan, Ronald Cheong Kin Wang, Zhe Chen, Hao Computer Vision and Pattern Recognition The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer diagnosis, and prognosis assessment. While these models show remarkable potential across cancer diagnostics and prognostics, their clinical translation faces critical challenges including variability in optimal model across cancer types, potential data leakage in evaluation, and lack of standardized benchmarks. Without rigorous, unbiased evaluation, even the most advanced PFMs risk remaining confined to research settings, delaying their life-saving applications. Existing benchmarking efforts remain limited by narrow cancer-type focus, potential pretraining data overlaps, or incomplete task coverage. We present PathBench, the first comprehensive benchmark addressing these gaps through: multi-center in-hourse datasets spanning common cancers with rigorous leakage prevention, evaluation across the full clinical spectrum from diagnosis to prognosis, and an automated leaderboard system for continuous model assessment. Our framework incorporates large-scale data, enabling objective comparison of PFMs while reflecting real-world clinical complexity. All evaluation data comes from private medical providers, with strict exclusion of any pretraining usage to avoid data leakage risks. We have collected 15,888 WSIs from 8,549 patients across 10 hospitals, encompassing over 64 diagnosis and prognosis tasks. Currently, our evaluation of 19 PFMs shows that Virchow2 and H-Optimus-1 are the most effective models overall. This work provides researchers with a robust platform for model development and offers clinicians actionable insights into PFM performance across diverse clinical scenarios, ultimately accelerating the translation of these transformative technologies into routine pathology practice. |
| title | PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.20202 |