Spatially-Aware Mixture of Experts with Log-Logistic Survival Modeling for Whole-Slide Images
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 16 references that are in the map. First posted on arXiv 9 Nov 2025.
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References · 13
- 2017Attention Is All You Needcited by 59,932
- 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
- 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
- 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
- 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
- 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 148
- 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
- 2022Survival Mixture Density Networkscited by 9
- 2024SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Imagescited by 8
- 2025OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transportcited by 8
- 2024PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Imagescited by 2
- 2025CoC: Chain-of-Cancer based on Cross-Modal Autoregressive Traction for Survival Predictioncited by 2
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- 2026Anchor-driven Multi-modal Multi-scale Expert Selection for Survival Prediction
- 2026MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
- 2025Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillation
- 2026Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images
- 2026Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
- 2026Geometry-Aware State Space Model: A New Paradigm for Whole-Slide Image Representation
- 2026MMSF: Multitask and Multimodal Supervised Framework for WSI Classification and Survival Analysis
- 2026SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling