通过历史病例证据提升医学影像筛查的可解释性与准确率
Evidential Reasoning Advances Interpretable Real-World Disease Screening
- 基于历史病例区域证据构建双重知识库，实现回顾性可解释推理
- 在临床级召回率下显著提升特异性，优于现有模型
- 无需事后注意力图，直接用对比检索生成异常定位图
疾病筛查对临床早期发现和及时干预至关重要。然而，当前大多数医学图像筛查模型存在可解释性差、性能不足的问题，缺乏有效参考历史病例或提供透明推理路径的机制。为此，我们提出 EviScreen，一种基于区域证据的疾病筛查推理框架。该框架通过双知识库检索历史病例中的区域证据，实现回溯性可解释性；后续的证据感知推理模块结合当前病例与历史证据进行预测，从而提升筛查性能。此外，EviScreen 不依赖事后显著性图，而是通过对比检索生成异常定位图，增强定位可解释性。在我们精心构建的真实世界疾病筛查基准上，该方法在临床级召回率下实现了显著更高的特异性。代码已公开于 https://github.com/DopamineLcy/EviScreen。
原文摘要 · Abstract (English)
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.
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