Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
Ardhendu Sekhar, Vasu Soni, Keshav Aske, Shivam Madnoorkar, Pranav Jeevan, Amit Sethi

TL;DR
This paper presents a novel computational framework for survival prediction from whole-slide histopathology images, integrating spatially-aware patch selection, clustering, hierarchical attention, and a flexible survival model, achieving state-of-the-art results.
Contribution
It introduces four innovative methods to address spatial heterogeneity and complex survival distributions in WSIs, advancing personalized cancer prognosis.
Findings
Achieved state-of-the-art concordance indices on TCGA cohorts
Improved calibration and interpretability of survival predictions
Outperformed existing histology-only and multimodal models
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…
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Taxonomy
TopicsAI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Cell Image Analysis Techniques
