通过双原型融合提升病理切片生存预测的可解释性与不确定性感知能力
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
- 设计双原型结构融合病理图像块特征，生成带不确定性的生存区间
- 在5个公开数据集上达到最高一致性指数和最低布里尔分数
- 提供像素级解释图、组件原型和风险聚合分析，适合临床辅助决策
病理全切片图像（WSIs）因其在细胞与组织层面的完整组织学信息，被广泛用于癌症生存分析，支持定量、大规模且预后丰富的肿瘤特征挖掘。然而，现有大多数WSI生存分析方法存在可解释性差、忽略异质性切片中的预测不确定性等问题。本文提出DPsurv，一种双原型证据融合网络，可输出带有不确定性的生存区间，并通过切片块原型分配图、组件原型及组件级相对风险聚合实现多层级可解释性。在五个公开数据集上的实验表明，DPsurv在均值一致性指数上表现最优，均值集成布里尔分数最低，验证了其有效性和可靠性。预测结果的解释覆盖特征、推理与决策层面，显著提升模型可信度与透明度。
原文摘要 · Abstract (English)
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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