Deep Counterfactual-Guided MRI Feature Representation and Quantitatively Interpretable Alzheimer’s Disease Prediction

Abstract

Deep learning for Alzheimer's disease (AD) prediction has provided timely prevention of disease progression yet still demands attentive interpretability. Recently, counterfactual reasoning has increasingly been exploited in medical research by providing refined visual explanatory maps. However, such visual explanatory maps alone are not self-sufficient unless we can intuitively demonstrate their validity via quantitative features. For this, we first synthesize the counterfactual-labeled structural MRI using our proposed framework. We further transform it as a gray matter density map to precisely measure its volumetric changes over the parcellated region of interests (ROIs). Furthermore, to boost the effectiveness of selected ROIs while promoting interpretability and achieving comparable predictive performance to deep learning methods, we devise a novel lightweight counterfactual-guided attentive feature representation constructed to a linear classifier. Thus, our framework provides an AD-relatedness index for each ROI, offering an intuitive understanding of brain status for an individual subject and across subjects regarding AD.