  Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive
Impairment (MCI). Early detection of AD is crucial for making treatment
decisions. However, most of the literature on computer-assisted detection of AD
focuses on classifying brain images into one of three major categories:
healthy, MCI, and AD; or categorizing MCI patients into (1) progressive: those
who progress from MCI to AD at a future examination time, and (2) stable: those
who stay as MCI and never progress to AD. This misses the opportunity to
accurately identify the trajectory of progressive MCI patients. In this paper,
we revisit the brain image classification task for AD identification and
re-frame it as an ordinal classification task to predict how close a patient is
to the severe AD stage. To this end, we select progressive MCI patients from
the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an
ordinal dataset with a prediction target that indicates the time to progression
to AD. We train a Siamese network model to predict the time to onset of AD
based on MRI brain images. We also propose a Weighted variety of Siamese
network and compare its performance to a baseline model. Our evaluations show
that incorporating a weighting factor to Siamese networks brings considerable
performance gain at predicting how close input brain MRI images are to
progressing to AD. Moreover, we complement our results with an interpretation
of the learned embedding space of the Siamese networks using a model
explainability technique.
