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A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals

Authors
Oh, KwanseokHeo, Da-WoonMulyadi, Ahmad WisnuJung, WonsikKang, EunsongLee, Kun HoSuk, Heung-Il
Issue Date
Apr-2025
Publisher
Academic Press
Keywords
Alzheimer's disease; Counterfactual reasoning; Quantitative feature-based in-depth analysis; Counterfactual-guided attention
Citation
NeuroImage, v.309
Journal Title
NeuroImage
Volume
309
URI
http://scholarworks.bwise.kr/kbri/handle/2023.sw.kbri/1258
DOI
10.1016/j.neuroimage.2025.121077
ISSN
1053-8119
1095-9572
Abstract
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. Counterfactual reasoning has recently 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. It offers an intuitive understanding of brain status for an individual patient and across patient groups concerning AD progression.
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