  Increasing effort in brain image analysis has been dedicated to early
diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing
studies have been focusing on binary classification problems, e.g.,
distinguishing AD patients from normal control (NC) elderly or mild cognitive
impairment (MCI) individuals from NC elderly. However, identifying individuals
with AD and MCI, especially MCI individuals who will convert to AD (progressive
MCI, pMCI), in a single setting, is needed to achieve the goal of early
diagnosis of AD. In this paper, we propose a deep ordinal ranking model for
distinguishing NC, stable MCI (sMCI), pMCI, and AD at an individual subject
level, taking into account the inherent ordinal severity of brain degeneration
caused by normal aging, MCI, and AD, rather than formulating the classification
as a multi-category classification problem. The proposed deep ordinal ranking
model focuses on the hippocampal morphology of individuals and learns
informative and discriminative features automatically. Experiment results based
on a large cohort of individuals from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) indicate that the proposed method can achieve better
performance than traditional multi-category classification techniques using
shape and radiomics features from structural magnetic resonance imaging (MRI)
data.
