Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Graph Variational Autoencoder with Contrastive Learning

arXiv:2404.00785v1 Announce Type: cross
Abstract: This paper presents a comprehensive study focused on disentangling hippocampal shape variations from diffusion tensor imaging (DTI) datasets within the context of neurological disorders. Leveraging a Graph Variational Autoencoder (VAE) enhanced with Supervised Contrastive Learning, our approach aims to improve interpretability by disentangling two distinct latent variables corresponding to age and the presence of diseases. In our ablation study, we investigate a range of VAE architectures and contrastive loss functions, showcasing the enhanced disentanglement capabilities …

abstract arxiv autoencoder context cs.cv cs.lg datasets diffusion graph imaging paper q-bio.nc study tensor type vae