Abstract
Longitudinal imaging studies, where serial (multiple) scans are collected on each individual, are becoming increasingly widespread. The field of machine learning has in general neglected the longitudinal design, since many algorithms are built on the assumption that each datapoint is an independent sample. Thus, the application of general purpose machine learning tools to longitudinal image data can be sub-optimal. Here, we present a novel machine learning algorithm designed to handle longitudinal image datasets. Our approach builds on a sparse Bayesian image-based prediction algorithm. Our empirical results demonstrate that the proposed method can offer a significant boost in prediction performance with longitudinal clinical data.
