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arXiv · 2606.19966

Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis

Yucheng Xing·Ling Huang·Pei Liu·Jingying Ma·Jiaxing Xu·Kai He·Mengling Feng
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Abstract

Whole-slide images (WSIs) are widely used for computational cancer prognosis. However, most existing methods primarily focus on in-domain performance and fail to generalize across clinical centers. This limitation stems from their reliance on pixel-derived representations that are highly susceptible to domain-specific artifacts caused by staining protocols and scanner hardware. We hypothesize that high-level pathology semantics, such as tumor grade and micro-environmental architecture, provide a domain-invariant semantic representation that mirrors the robust diagnostic logic of human pathologists. Therefore, we propose a Semantic-Anchored Evidential Fusion Survival (SAEFS) framework, where SAEFS derives semantic anchors from WSIs via Visual Question Answering (VQA), employs a dual-stream WSI evidence extraction architecture, uses Dirichlet-based Subjective Logic to model uncertainty, and fuses semantic and visual evidence through a cautious conjunction rule to avoid overconfident fusion from correlated sources. Trained exclusively on one source domain and evaluated zero-shot across four unseen domains, SAEFS consistently outperforms state-of-the-art models both in prediction accuracy and reliability, improving the average C-index by 10.2%. Quantitative analyses further show that VQA-derived semantic features exhibit significantly lower cross-center divergence than pixel-derived features, highlighting their robustness for cross-center clinical applications.

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Yucheng Xing, Ling Huang, Pei Liu, Jingying Ma, Jiaxing Xu, Kai He, Mengling Feng. 2026-09-22. Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis. https://arxiv.org/abs/2606.19966

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