A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
This work addresses the need for quantitative interpretability in Alzheimer's disease prediction for medical researchers and clinicians, though it is incremental in combining existing counterfactual reasoning with new quantitative features.
The study tackled the problem of interpreting deep learning predictions for Alzheimer's disease by developing a framework that synthesizes counterfactual-labeled structural MRIs and transforms them into gray matter density maps to measure volumetric changes in brain regions, achieving comparable predictive performance to deep learning methods.
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.