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

10 Citations (Scopus)


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
Publication statusPublished - 2023 Jun

Bibliographical note

Publisher Copyright:
© 2023


  • Alzheimer's Disease
  • Explainable AI
  • Prototype Learning

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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