Deep Learning Models To Study The Early Stages Of Parkinson'S Disease

Current physio-pathological data suggest that Parkinson's Disease (PD) symptoms are related to important alterations in subcortical brain structures. However, structural changes in these small regions remain difficult to detect for neuroradiologists, in particular, at the early stages of the disease (de novo PD patients). The absence of a reliable ground truth at the voxel level prevents the application of traditional supervised deep learning techniques. In this work, we consider instead an anomaly detection approach and show that auto-encoders (AE) could provide an efficient anomaly scoring to discriminate de novo PD patients using quantitative Magnetic Resonance Imaging (MRI) data.