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arXiv · 2105.01128

Fusing multimodal neuroimaging data with a variational autoencoder

Eloy Geenjaar·Noah Lewis·Zening Fu·Rohan Venkatdas·Sergey Plis·Vince Calhoun
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Abstract

Neuroimaging studies often involve the collection of multiple data modalities. These modalities contain both shared and mutually exclusive information about the brain. This work aims at finding a scalable and interpretable method to fuse the information of multiple neuroimaging modalities using a variational autoencoder (VAE). To provide an initial assessment, this work evaluates the representations that are learned using a schizophrenia classification task. A support vector machine trained on the representations achieves an area under the curve for the classifier's receiver operating characteristic (ROC-AUC) of 0.8610.

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Eloy Geenjaar, Noah Lewis, Zening Fu, Rohan Venkatdas, Sergey Plis, Vince Calhoun. 2021-05-03. Fusing multimodal neuroimaging data with a variational autoencoder. https://arxiv.org/abs/2105.01128

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