DPsurv: 双原型证据融合用于不确定性感知和可解释的全切片图像生存预测
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
AI总结：
提出DPsurv双原型证据融合网络，通过不确定性感知的生存区间预测和基于补丁原型分配图、组件原型及组件级相对风险聚合的可解释性，在五个公开数据集上取得最佳一致性指数和积分Brier分数。
摘要：
病理全切片图像（WSIs）因其在细胞和组织水平上全面的组织病理学信息而被广泛用于癌症生存分析，能够进行定量、大规模且预后丰富的肿瘤特征分析。然而，现有大多数WSI生存分析方法可解释性有限，且常常忽略异质性切片图像中的预测不确定性。本文提出DPsurv，一种双原型全切片图像证据融合网络，输出不确定性感知的生存区间，同时通过补丁原型分配图、组件原型和组件级相对风险聚合实现预测的解释。在五个公开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积分Brier分数，验证了DPsurv的有效性和可靠性。预测结果的解释在特征、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。
英文摘要：
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.
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