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Deep Learning Models to Study the Early Stages of Parkinsons Disease
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Deep Learning Models to Study the Early Stages of Parkinsons 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 structures remain difficult to detect for neuro-radiologists, 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.
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 structures remain difficult to detect for neuro-radiologists, 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.
