  Mild cognitive impairment (MCI) is often at high risk of progression to
Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI)
typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by
whether or not an MCI patient will progress to AD after a long follow-up.
However, prospectively acquiring MCI subtype data is time-consuming and
resource-intensive; the resultant small datasets could lead to severe
overfitting and difficulty in extracting discriminative information. Inspired
by that various longitudinal biomarkers and cognitive measurements present an
ordinal pathway on AD progression, we propose a novel Hybrid-granularity
Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression
for MCI progression prediction. First, HOPE learns an ordinal metric space that
enables progression prediction by prototype comparison. Second, HOPE leverages
a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via
effectively integrating instance-to-instance ordinality, instance-to-class
compactness, and class-to-class separation. Third, to make the prototype
learning more stable, HOPE employs an exponential moving average strategy to
learn the global prototypes of NC and AD dynamically. Experimental results on
the internal ADNI and the external NACC datasets demonstrate the superiority of
the proposed HOPE over existing state-of-the-art methods as well as its
interpretability. Source code is made available at
https://github.com/thibault-wch/HOPE-for-mild-cognitive-impairment.
