Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Recently, counterfactual reasoning has gained increasing attention in medical research because of its ability to provide a refined visual explanatory map. However, such visual explanatory maps based on visual inspection alone are insufficient unless we intuitively demonstrate their medical or neuroscientific validity via quantitative features. In this study, we synthesize the counterfactual-labeled structural MRIs using our proposed framework and transform it into a gray matter density map to measure its volumetric changes over the parcellated region of interest (ROI). We also devised a lightweight linear classifier to boost the effectiveness of constructed ROIs, promoted quantitative interpretation, and achieved comparable predictive performance to DL methods. Throughout this, our framework produces an ``AD-relatedness index'' for each ROI and offers an intuitive understanding of brain status for an individual patient and across patient groups with respect to AD progression.

通过使用我们提出的框架，综合对应于反事实的结构性磁共振图像，并将其转化为灰质密度图来衡量其在感兴趣区域（ROI）中的体积变化，本研究通过采用一个轻量级的线性分类器来增强构建的ROIs的有效性，进而实现了定量诠释，达到了与深度学习方法相当的预测性能，该框架为每个感兴趣区域提供了一个“与阿尔茨海默病相关性指数”，从而直观地理解个体患者和患者组与阿尔茨海默病进展的大脑状态。

基于深层反事实推断的阿尔茨海默病预测的量化可解释模型