[论文] Evidential Reasoning Advances Interpretable Real-World Disease Screeni...

论文概要

研究领域: CV 作者: Chenyu Lian, Hong-Yu Zhou, Jing Qin 发布时间: 2026-05-16 arXiv: 2505.08637

中文摘要

疾病筛查在临床实践中对早期发现和及时干预至关重要。然而，大多数当前的医学影像筛查模型存在可解释性有限和性能欠佳的问题。它们通常缺乏有效机制来参考历史病例或提供透明的推理路径。为解决这些挑战，我们引入了EviScreen，一个用于疾病筛查的证据推理框架，利用历史病例的区域级证据。所提出的EviScreen通过从双重知识库检索的区域证据提供回顾性可解释性。利用这一证据机制，后续的证据感知推理模块使用当前病例和历史病例的证据进行预测，从而增强疾病筛查性能。此外，EviScreen不依赖事后显著性图，而是通过利用对比检索得到的异常图来增强定位可解释性。我们的方法在我们精心建立的真实世界疾病筛查基准上实现了卓越性能，在临床级召回率下产生显著更高的特异性。代码公开可在 https://github.com/DopamineLcy/EviScreen 获取。

原文摘要

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 enhancin...

自动采集于 2026-05-16

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