Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning

Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh, Jee Seok Yoon, Kun Ho Lee, Heung Il Suk

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)

    Abstract

    Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

    Original languageEnglish
    Article number120073
    JournalNeuroImage
    Volume273
    DOIs
    Publication statusPublished - 2023 Jun

    Bibliographical note

    Publisher Copyright:
    © 2023

    Keywords

    • Alzheimer's Disease
    • Explainable AI
    • Prototype Learning

    ASJC Scopus subject areas

    • Neurology
    • Cognitive Neuroscience

    Fingerprint

    Dive into the research topics of 'Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning'. Together they form a unique fingerprint.

    Cite this