Prognostic saliency-driven hypergraph neural network for survival prediction via vision foundation model

Precise survival prediction from Whole Slide Images (WSIs) is pivotal for precision oncology yet remains challenging due to the gigapixel resolution and label scarcity. Current approaches are often hindered by two fundamental limitations: the domain misalignment between natural-image-pretrained vision foundation models and histopathological data, and the lack of high-order correlations modeling among sparsely distributed prognostic regions. In this paper, we propose a dual-stage Prognostic Saliency-driven Hypergraph Neural Network (ProSH-Net) for WSI-based survival prediction. A parameter-efficient distillation-based domain-adaptive pre-training paradigm is proposed to fully leverage the representation capability of vision foundation models while ensuring sensitivity to fine-grained morphological patterns. Subsequently, a saliency-aware hypergraph neural network is proposed to orchestrate feature aggregation. Different from traditional graphs, our method constructs hyperedges guided by semantic prototypes and spatial priors, explicitly modeling multi-to-multi interactions among patches. By injecting patch-level saliency scores into the message-passing mechanism, ProSH-Net effectively enhances critical prognostic signals. Extensive experiments on public benchmarks demonstrate the superiority of our proposed method over state-of-the-art alternatives.