MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer's disease progression modelling

Gerard Martí-Juan, Marco Lorenzi, Gemma Piella, Alzheimer's Disease Neuroimaging Initiative

Abstract: Highlights•A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed model was evaluated on synthetic and real datasets.•Model outperforms a set of baselines for missing data reconstruction across modalities.