@inproceedings{
kumar2023mmnormvae,
title={mmNorm{VAE}: Normative Modeling on Multimodal Neuroimaging Data using Variational Autoencoders},
author={Sayantan Kumar and Philip Payne and Aristeidis Sotiras},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=khB5CQidql}
}
Normative modelling is a popular method for studying brain disorders like Alzheimer's Disease (AD) where the normal brain patterns of cognitively normal subjects are modelled and can be used at subject-level to detect deviations relating to disease pathology. So far, deep learning-based normative frameworks have largely been applied on a single imaging modality. We aim to design a multi-modal normative modelling framework based on multimodal variational autoencoders (mmNormVAE) where disease abnormality is aggregated across multiple neuroimaging modalities (T1-weighted and T2-weighted MRI) and subsequently used to estimate subject-level neuroanatomical deviations due to AD.
mmNormVAE: Normative Modeling on Multimodal Neuroimaging Data using Variational Autoencoders
Sayantan Kumar Philip Payne Aristeidis Sotiras