标题： DPsurv：双原型证据融合用于不确定感知和可解释的全切片图像生存预测 显示英文标题

标题： DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction

摘要： 病理学全切片图像（WSIs）被广泛用于癌症生存分析，因为它们在细胞和组织水平上提供了全面的组织病理学信息，使得能够进行定量、大规模和预后丰富的肿瘤特征分析。 然而，大多数现有的WSI生存分析方法在可解释性方面存在局限，并且常常忽略异质切片图像中的预测不确定性。 在本文中，我们提出了DPsurv，这是一种双原型全切片图像证据融合网络，能够输出具有不确定性的生存区间，同时通过切片原型分配图、组件原型和组件级相对风险聚合来实现预测的可解释性。 在五个公开可用的数据集上的实验取得了最高的平均一致性指数和最低的平均综合Brier得分，验证了DPsurv的有效性和可靠性。 预测结果的可解释性在特征、推理和决策层面提供了透明度，从而增强了DPsurv的可信度和可解释性。 显示英文摘要

摘要： 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 uncertainty-aware survival intervals, while enabling interpretation of predictions through patch prototype assignment maps, component prototypes, and component-wise relative risk aggregation. Experiments on five publicly available datasets achieve the highest mean concordance index and the lowest mean integrated Brier score, validating the effectiveness and reliability of DPsurv. The interpretation of prediction results provides transparency at the feature, reasoning, and decision levels, thereby enhancing the trustworthiness and interpretability of DPsurv.