Pathology foundation models (PFMs) achieve strong performance on diverse histopathology tasks, but their sensitivity to hospital-specific domain shifts remains underexplored. We systematically evaluate state-of-the-art PFMs on TCGA patch-level datasets and introduce a lightweight adversarial adaptor to remove hospital-related domain information from latent representations. Experiments show that, while disease classification accuracy is largely maintained, the adaptor effectively reduces hospital-specific bias, as confirmed by t-SNE visualizations. Our study establishes a benchmark for assessing cross-hospital robustness in PFMs and provides a practical strategy for enhancing generalization under heterogeneous clinical settings. Our code is available at https://github.com/MengRes/pfm_domain_bias.

翻译：病理学基础模型（PFMs）在多种组织病理学任务中展现出卓越性能，但其对医院特异性领域偏移的敏感性尚未得到充分探究。本研究系统评估了前沿PFMs在TCGA补丁级数据集上的表现，并引入一种轻量级对抗适配器以从潜在表示中消除医院相关的领域信息。实验表明，在基本保持疾病分类准确率的同时，该适配器能有效降低医院特异性偏差，此结论通过t-SNE可视化得到验证。本研究为评估PFMs的跨医院鲁棒性建立了基准，并为提升模型在异质临床环境下的泛化能力提供了实用策略。相关代码已发布于https://github.com/MengRes/pfm_domain_bias。