当前的病理基础模型对医疗中心的差异性缺乏鲁棒性。

Current Pathology Foundation Models are unrobust to Medical Center Differences

January 29, 2025

作者: Edwin D. de Jong, Eric Marcus, Jonas Teuwen

cs.AI

摘要

病理基础模型（FMs）在医疗保健领域具有巨大潜力。在它们能够应用于临床实践之前，确保其对医疗中心之间的差异具有稳健性至关重要。我们衡量病理基础模型是否专注于生物特征，如组织和癌症类型，还是专注于由染色程序和其他差异引入的众所周知的医疗中心特征。我们引入了鲁棒性指数。这一新颖的鲁棒性度量反映了生物特征主导混杂特征的程度。我们评估了十个当前公开可用的病理基础模型。我们发现，所有当前评估的病理基础模型都很强烈地代表了医疗中心。观察到了鲁棒性指数上的显著差异。到目前为止，只有一个模型的鲁棒性指数大于一，意味着生物特征主导混杂特征，但仅略微如此。描述了一种定量方法来衡量医疗中心差异对基于FM的预测性能的影响。我们分析了鲁棒性对下游模型分类性能的影响，发现癌症类型分类错误并非随机发生，而是特别归因于同一医疗中心的混杂因素：来自同一医疗中心的其他类别的图像。我们可视化了FM嵌入空间，并发现这些空间更多地由医疗中心而不是生物因素组织。因此，原始医疗中心比组织来源和癌症类型更准确地被预测。本文介绍的鲁棒性指数旨在推动向具有稳健性和可靠性的病理基础模型的临床采用的进展。

English

Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between medical centers. We measure whether pathology FMs focus on biological features like tissue and cancer type, or on the well known confounding medical center signatures introduced by staining procedure and other differences. We introduce the Robustness Index. This novel robustness metric reflects to what degree biological features dominate confounding features. Ten current publicly available pathology FMs are evaluated. We find that all current pathology foundation models evaluated represent the medical center to a strong degree. Significant differences in the robustness index are observed. Only one model so far has a robustness index greater than one, meaning biological features dominate confounding features, but only slightly. A quantitative approach to measure the influence of medical center differences on FM-based prediction performance is described. We analyze the impact of unrobustness on classification performance of downstream models, and find that cancer-type classification errors are not random, but specifically attributable to same-center confounders: images of other classes from the same medical center. We visualize FM embedding spaces, and find these are more strongly organized by medical centers than by biological factors. As a consequence, the medical center of origin is predicted more accurately than the tissue source and cancer type. The robustness index introduced here is provided with the aim of advancing progress towards clinical adoption of robust and reliable pathology FMs.