Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images

• URL: http://arxiv.org/abs/2511.06266v3
• Date: Mon, 17 Nov 2025 07:13:24 GMT
• Title: Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
• Authors: Ardhendu Sekhar, Vasu Soni, Keshav Aske, Shivam Madnoorkar, Pranav Jeevan, Amit Sethi,
• Abstract summary: We introduce a comprehensive computational pathology framework that addresses limitations through four complementary innovations.<n>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.<n>The framework further provides improved calibration and interpretability, advancing the use of WSIs for personalized cancer prognosis.
• Score: 6.825656149756289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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