目前的病理基礎模型對醫療中心的差異性缺乏魯棒性。

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

病理基礎模型（FMs）對醫療保健領域具有巨大潛力。在它們應用於臨床實踐之前，確保它們對醫療中心之間的變化具有韌性至關重要。我們衡量病理FMs是否專注於生物特徵，如組織和癌症類型，或者專注於由染色程序和其他差異引入的眾所周知的混淆醫療中心特徵。我們引入了韌性指數。這個新穎的韌性度量反映了生物特徵主導混淆特徵的程度。對十個當前公開可用的病理FMs進行評估。我們發現所有目前評估的病理基礎模型都很好地代表了醫療中心。觀察到韌性指數存在顯著差異。到目前為止，只有一個模型的韌性指數大於一，這意味著生物特徵主導混淆特徵，但僅輕微。描述了一種量化方法來衡量醫療中心差異對基於FM的預測性能的影響。我們分析了韌性對下游模型分類性能的影響，發現癌症類型分類錯誤並非隨機的，而是特別歸因於相同中心的混淆因素：來自同一醫療中心的其他類別的圖像。我們可視化FM嵌入空間，發現這些空間更多地由醫療中心而不是生物因素組織。因此，原始醫療中心比組織來源和癌症類型更準確地被預測。這裡介紹的韌性指數旨在推動向具有韌性和可靠性的病理FMs的臨床採用的進展。

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.