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Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic processowing to its innate traits of irreversibility with subtle and gradualprogression. These characteristics make AD biomarker identification fromstructural brain imaging (e.g., structural MRI) scans quite challenging.Furthermore, there is a high possibility of getting entangled with normalaging. We propose a novel deep-learning approach through eXplainable ADLikelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIsusing clinically-guided prototype learning. Specifically, we establish a set oftopologically-aware prototypes onto the clusters of latent clinical features,uncovering an AD spectrum manifold. We then measure the similarities betweenlatent clinical features and well-established prototypes, estimating a "pseudo"likelihood map. By considering this pseudo map as an enriched reference, weemploy an estimating network to estimate the AD likelihood map over a 3D sMRIscan. Additionally, we promote the explainability of such a likelihood map byrevealing a comprehensible overview from two perspectives: clinical andmorphological. During the inference, this estimated likelihood map served as asubstitute over unseen sMRI scans for effectively conducting the downstreamtask while providing thorough explainable states.

XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning