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

Whole-slide images (WSIs) are widely used for computa-tional cancer prognosis. However, current research primarily focuses onin-domain performance and fails to generalize across clinical centers. Thislimitation stems from their reliance on pixel-derived representations thatare highly susceptible to domain-specific artifacts caused by stainingprotocols and scanner hardware. We hypothesize that high-level pathol-ogy semantics, such as tumor grade and micro-environmental architec-ture, provide a domain-invariant semantic representation that mirrorsthe robust diagnostic logic of human pathologists. Therefore, we proposea Semantic-Anchored Evidential Fusion Survival (SAEFS) framework,where SAEFS derives semantic anchors from WSIs via Visual QuestionAnswering (VQA), employs a dual-stream WSI evidence extraction archi-tecture, uses Dirichlet-based Subjective Logic to model uncertainty, andfuses semantic and visual evidence through a cautious conjunction rule toavoid overconfident fusion from correlated sources. Trained exclusively onone source domain and evaluated zero-shot across four unseen domains,SAEFS consistently outperforms state-of-the-art models both in predic-tion accuracy and reliability, improving the average C-index by 10.2%.Quantitative analyses further show that VQA-derived semantic featuresexhibit significantly lower cross-center divergence than pixel-derived fea-tures, highlighting their robustness for cross-center clinical applications.The code is available at https://github.com/YuchengXing99/SAEFS.