Abstract
Deep learning offers a powerful approach for analyzing hippocampal changes in
Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless,
an input format needs to be selected to pass the image information to the neural
network, which has wide ramifications for the analysis, but has not been
evaluated yet. We compare five hippocampal representations (and their respective
tailored network architectures) that span from raw images to geometric
representations like meshes and point clouds. We performed a thorough evaluation
for the prediction of AD diagnosis and time-to-dementia prediction with
experiments on an independent test dataset. In addition, we evaluated the ease
of interpretability for each representation–network pair. Our results show that
choosing an appropriate representation of the hippocampus for predicting
Alzheimer’s disease with deep learning is crucial, since it impacts performance
and ease of interpretation.
Publication
Scientific Reports