Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
This work addresses personalized cancer prognosis for patients using histopathology images, representing a strong specific gain rather than a broad paradigm shift.
The paper tackled accurate survival prediction from whole-slide images by introducing a computational pathology framework with innovations like quantile-gated patch selection and expert-driven mixture of log-logistics, achieving state-of-the-art performance with time-dependent concordance indices of 0.644 on LUAD, 0.751 on KIRC, and 0.752 on BRCA.
Accurate survival prediction from histopathology whole-slide images (WSIs) remains challenging due to their gigapixel resolution, strong spatial heterogeneity, and complex survival distributions. We introduce a comprehensive computational pathology framework that addresses these limitations through four complementary innovations: (1) Quantile-Gated Patch Selection for dynamically identifying prognostically relevant regions, (2) Graph-Guided Clustering to group patches by spatial and morphological similarity, (3) Hierarchical Context Attention to model both local tissue interactions and global slide-level context, and (4) an Expert-Driven Mixture of Log-Logistics module that flexibly models complex survival distributions. Across large TCGA cohorts, our method achieves state-of-the-art performance, yielding time-dependent concordance indices of 0.644 on LUAD, 0.751 on KIRC, and 0.752 on BRCA, consistently outperforming both histology-only and multimodal baselines. The framework further provides improved calibration and interpretability, advancing the use of WSIs for personalized cancer prognosis.