Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis

• URL: http://arxiv.org/abs/2602.23557v2
• Date: Mon, 02 Mar 2026 09:07:37 GMT
• Title: Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
• Authors: Bin Xu, Yufei Zhou, Boling Song, Jingwen Sun, Yang Bian, Cheng Lu, Ye Wu, Jianfei Tu, Xiangxue Wang,
• Abstract summary: Hierarchical Multi-scale Knowledge-aware Graph Network (HMKGN) models multi-scale interactions and spatially hierarchical relationships within whole-slide images (WSIs) for cancer prognostication.<n>We evaluate HMKGN on four TCGA cohorts (KIRC, LGG, PAAD, and STAD) for survival prediction.
• Score: 7.719549259296449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a Hierarchical Multi-scale Knowledge-aware Graph Network (HMKGN) that models multi-scale interactions and spatially hierarchical relationships within whole-slide images (WSIs) for cancer prognostication. Unlike conventional attention-based MIL, which ignores spatial organization, or graph-based MIL, which relies on static handcrafted graphs, HMKGN enforces a hierarchical structure with spatial locality constraints, wherein local cellular-level dynamic graphs aggregate spatially proximate patches within each region of interest (ROI) and a global slide-level dynamic graph integrates ROI-level features into WSI-level representations. Moreover, multi-scale integration at the ROI level combines coarse contextual features from broader views with fine-grained structural representations from local patch-graph aggregation. We evaluate HMKGN on four TCGA cohorts (KIRC, LGG, PAAD, and STAD; N=513, 487, 138, and 370) for survival prediction. It consistently outperforms existing MIL-based models, yielding improved concordance indices (10.85% better) and statistically significant stratification of patient survival risk (log-rank p < 0.05).

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