Revealing Patterns of Symptomatology in Parkinson's Disease: A Latent Space Analysis with 3D Convolutional Autoencoders. (arXiv:2305.07038v1 [eess.IV])

This work proposes the use of 3D convolutional variational autoencoders
(CVAEs) to trace the changes and symptomatology produced by neurodegeneration
in Parkinson's disease (PD). In this work, we present a novel approach to
detect and quantify changes in dopamine transporter (DaT) concentration and its
spatial patterns using 3D CVAEs on Ioflupane (FPCIT) imaging. Our approach
leverages the power of deep learning to learn a low-dimensional representation
of the brain imaging data, which then is linked to different symptom categories
using …

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