DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
arXiv:2510.00053v2 Announce Type: replace-cross Abstract: Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling quantitative, large-scale, and prognostically rich tumor feature analysis. However, most existing methods in WSI survival analysis struggle with limited interpretability and often overlook predictive uncertainty in heterogeneous slide images. In this paper, we propose DPsurv, a dual-prototype whole-slide image evidential fusion network that outputs unce
The continuous advancements in AI and deep learning are enabling more sophisticated analysis of complex medical images, making personalized medicine more viable.
This development offers a pathway to more accurate and interpretable cancer prognoses, which can significantly improve treatment planning and patient outcomes.
Pathology analysis becomes more precise and less reliant on subjective interpretation, incorporating uncertainty quantification into critical medical decisions.
- · AI healthcare innovators
- · Oncology patients
- · Medical research institutions
- · Diagnostic imaging companies
- · Traditional pathology methods
- · Companies resistant to AI adoption
Improved early cancer detection and personalized treatment strategies become more commonplace.
Reduced healthcare costs due to more effective treatments and fewer misdiagnoses.
Enhanced AI systems could integrate real-time patient data with WSI analysis, leading to dynamic, adaptive treatment protocols.
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