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Paper · arXiv

HGASurv: Hierarchical Granularity-Aware Multimodal Integration for Survival Prediction

Multimodal survival prediction jointly analyzes histopathological whole-slide images (WSIs), genomic profiles, and clinical variables to stratify patients by risk. However, existing approaches often inadequately account for granularity differences across modalities, particularly in spatial organization and feature dimensionality. Within pathology, aggregating WSI patch features without preserving their spatial arrangement can obscure tissue architecture; across modalities, directly concatenating low-dimensional clinical variables with high-dimensional visual and genomic features may limit the contribution of clinical information to prediction. To address these challenges, we propose HGASu...

AuthorsPeike Yu, Wuchao Liu, Jihong Guan et al.
Primary categorycs.CV
Published19 October 2026
Last updated at source20 October 2026
cs.CV
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  • 21 Oct 2026 Name: HGASurv: Hierarchical Granularity-Aware Integration for Multimodal Survival Prediction → HGASurv: Hierarchical Granularity-Aware Multimodal Integration for Survival Prediction
  • 21 Oct 2026 Description: Objective: Multimodal survival prediction jointly analyzes histopathological whole-slide images (WSIs), genomic profiles, and clinical variables to stratify patients by risk. However, existing approaches often inadequately account for granu → Multimodal survival prediction jointly analyzes histopathological whole-slide images (WSIs), genomic profiles, and clinical variables to stratify patients by risk. However, existing approaches often inadequately account for granularity diff

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