Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images

Misgina Tsighe Hagos, Niamh Belton, Ronan P. Killeen, Kathleen M. Curran, Brian Mac Namee

Abstract: 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 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 AD identification task 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 construct an ordinal dataset of progressive MCI patients with a prediction target that indicates the time to progression to AD. We train a Siamese network (SN) model to predict the time to onset of AD based on MRI brain images. We also propose a Weighted variety of SN and compare its performance to a baseline model. Our evaluations show that incorporating a weighting factor to SN brings considerable performance gain. Moreover, we complement our results with an interpretation of the learned embedding space of the SNs using a model explainability technique.