Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical images suffer from limited interpretability and suboptimal performance. They often lack effective mechanisms to reference historical cases or provide transparent reasoning pathways. To address these challenges, we introduce EviScreen, an evidential reasoning framework for disease screening that leverages region-level evidence from historical cases. The proposed EviScreen offers retrospection interpretability through regional evidence retrieved from dual knowledge banks. Using this evidential mechanism, the subsequent evidence-aware reasoning module makes predictions using both the current case and evidence from historical cases, thereby enhancing disease screening performance. Furthermore, rather than relying on post-hoc saliency maps, EviScreen enhances localization interpretability by leveraging abnormality maps derived from contrastive retrieval. Our method achieves superior performance on our carefully established benchmarks for real-world disease screening, yielding notably higher specificity at clinical-level recall. Code is publicly available at https://github.com/DopamineLcy/EviScreen.

翻译：疾病筛查对于临床实践中的早期发现和及时干预至关重要。然而，目前大多数医学影像筛查模型存在可解释性有限和性能欠佳的问题，它们常缺乏引用历史病例的有效机制或提供透明推理路径的能力。为应对这些挑战，我们提出了EviScreen——一种基于证据推理的疾病筛查框架，该框架利用来自历史病例的区域级证据。所提出的EviScreen通过从双知识库中检索的区域证据提供回顾性可解释性。借助这一证据机制，后续的证据感知推理模块利用当前病例和历史病例中的证据进行预测，从而提升疾病筛查性能。此外，EviScreen不依赖事后显著性图，而是利用对比检索产生的异常图增强定位可解释性。我们在精心构建的实际疾病筛查基准上取得了优越性能，在临床级召回率下获得了显著更高的特异性。代码已公开于https://github.com/DopamineLcy/EviScreen。