这篇文章对数字病理学中的基础模型（FM）进行了系统性调查，重点关注其对非生物技术特征（如扫描仪或染色差异）的鲁棒性。作者提出了 PathoROB 基准，包含三个新指标（如鲁棒性指数）和四个多中心数据集，旨在量化和评估20个现有FM的鲁棒性缺陷。研究发现，FM鲁棒性不足会导致诊断下游任务中出现重大错误，即“巧手汉斯”学习问题，进而妨碍临床安全采用。该工作还提出了 FM 鲁棒化框架，包括图像空间、表示空间和训练鲁棒化方法，以减轻这些缺陷，并强调鲁棒性评估和集成必须成为未来 FM 设计的核心原则。References: Kömen J, de Jong E D, Hense J, et al. Towards Robust Foundation Models for Digital Pathology[J]. arXiv preprint arXiv:2507.17845, 2025.
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