Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness.

ABSTRACT: Foundation models in computational pathology suffer from site-specific and demographic biases, which compromise their generalizability and fairness. We introduce FLEX, a framework that employs a task-specific information bottleneck, guided by visual and textual domain knowledge, to disentangle robust pathological features from these artifacts. Using three large cohorts (The Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium, and an in-house dataset) across 16 clinical tasks, totaling over 9,900 slides, we demonstrate that FLEX achieves superior zero-shot generalization to unseen external cohorts, significantly outperforming baselines and narrowing the performance gap between seen and unseen domains. A comprehensive fairness analysis confirms that FLEX also effectively mitiga

SUBMITTER: Huang Y 

PROVIDER: S-EPMC12749728 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature