Survival Modeling from Whole Slide Images via Patch-Level Graph Clustering and Mixture Density Experts

• URL: http://arxiv.org/abs/2507.16476v1
• Date: Tue, 22 Jul 2025 11:32:36 GMT
• Title: Survival Modeling from Whole Slide Images via Patch-Level Graph Clustering and Mixture Density Experts
• Authors: Ardhendu Sekhar, Vasu Soni, Keshav Aske, Garima Jain, Pranav Jeevan, Amit Sethi,
• Abstract summary: We introduce a modular framework for predicting cancer-specific survival from whole slide pathology images (WSIs)<n>To tackle large size of WSIs, we use dynamic patch selection via quantile-based thresholding for isolating prognostically informative tissue regions.<n>Thirdly, we use attention mechanisms that model both intra- and inter-cluster relationships to contextualize local features.
• Score: 3.6260816942800975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a modular framework for predicting cancer-specific survival from whole slide pathology images (WSIs) that significantly improves upon the state-of-the-art accuracy. Our method integrating four key components. Firstly, to tackle large size of WSIs, we use dynamic patch selection via quantile-based thresholding for isolating prognostically informative tissue regions. Secondly, we use graph-guided k-means clustering to capture phenotype-level heterogeneity through spatial and morphological coherence. Thirdly, we use attention mechanisms that model both intra- and inter-cluster relationships to contextualize local features within global spatial relations between various types of tissue compartments. Finally, we use an expert-guided mixture density modeling for estimating complex survival distributions using Gaussian mixture models. The proposed model achieves a concordance index of $0.712 \pm 0.028$ and Brier score of $0.254 \pm 0.018$ on TCGA-KIRC (renal cancer), and a concordance index of $0.645 \pm 0.017$ and Brier score of $0.281 \pm 0.031$ on TCGA-LUAD (lung adenocarcinoma). These results are significantly better than the state-of-art and demonstrate predictive potential of the proposed method across diverse cancer types.

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