Fusing multimodal neuroimaging data with a variational autoencoder
This work addresses the challenge of integrating multimodal brain data for improved analysis in neuroimaging, particularly for schizophrenia classification, but it appears incremental as it applies an existing VAE framework to this domain.
The paper tackled the problem of fusing multiple neuroimaging modalities by developing a scalable and interpretable method using a variational autoencoder, resulting in a schizophrenia classification task achieving an ROC-AUC of 0.8610.
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.