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.
View on arXiv@article{ma2025_2505.20202,
title={ PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology },
author={ Jiabo Ma and Yingxue Xu and Fengtao Zhou and Yihui Wang and Cheng Jin and Zhengrui Guo and Jianfeng Wu and On Ki Tang and Huajun Zhou and Xi Wang and Luyang Luo and Zhengyu Zhang and Du Cai and Zizhao Gao and Wei Wang and Yueping Liu and Jiankun He and Jing Cui and Zhenhui Li and Jing Zhang and Feng Gao and Xiuming Zhang and Li Liang and Ronald Cheong Kin Chan and Zhe Wang and Hao Chen },
journal={arXiv preprint arXiv:2505.20202},
year={ 2025 }
}