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Conference Papers Year : 2020

Deep learning models to study the early stages of Parkinson's disease

Abstract

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 autoencoders (AE) could provide an efficient anomaly scoring to discriminate de novo PD patients using quantitative Magnetic Resonance Imaging (MRI) data.
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Dates and versions

hal-03873699 , version 1 (27-11-2022)

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Verónica Muñoz Ramírez, Virgilio Kmetzsch, Florence Forbes, Michel Dojat. Deep learning models to study the early stages of Parkinson's disease. ISBI 2020 - IEEE International Symposium on Biomedical Imaging, Apr 2020, Iowa City, United States. ⟨10.1109/ISBI45749.2020.9098529⟩. ⟨hal-03873699⟩
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