Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer's Disease Identification

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression, hampering the AD biomarker identification from structural brain imaging (e.g., structural MRI) scans. We propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs using clinically-guided prototype learning. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. We then measure the similarities between latent clinical features and these prototypes to infer a "pseudo" AD likelihood map. Considering this pseudo map as an enriched reference, we employ an inferring network to estimate the AD likelihood map over a 3D sMRI scan. We further promote the explainability of such a likelihood map by revealing a comprehensible overview from two perspectives: clinical and morphological.