Increasing Feature Sparsity in Alzheimer's Disease Classification with Relevance-Guided Deep Learning

Using T1-weighted images we separated Alzheimer's patients (n=130) from healthy controls (n=375) by using a deep neural network and found that the preprocessing steps might introduce unwanted features to be used by the classifier. We systematically investigated the influence of registration and brain extraction on the learned features using a relevance map generator attached to the classification network. The results were compared to our relevance-guided training method. Relevance-guided training identifies sparser but substantially more relevant voxels, which improves the classification accuracy.