Prof. Emma Shujun WANG’s research on “Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness"
Research paper titled “Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness”, with Professor Emma Shujun WANG as one of the corresponding authors, was recently published in Nature Communications, an open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences.
“Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness”
Yanyan Huang, Weiqin Zhao, Zhengyu Zhang, Yihang Chen, Yu Fu, Feng Wu, Yuming Jiang, Li Liang*, Shujun Wang* and Lequan Yu*
Nature Communications 16: 11485 (2025). doi: 10.1038/s41467-025-66300-y
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 mitigates disparities across demographic groups. Furthermore, its versatility and scalability are proven through compatibility with various foundation models and multiple-instance learning architectures. Our work establishes FLEX as a promising solution for developing more generalizable and equitable pathology AI for diverse clinical settings.
