Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning
<|reference_start|>Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning: 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 of our approach. This evaluation uses synthetic 3D torus mesh data and real 3D hippocampal mesh datasets derived from the DTI hippocampal dataset. Our supervised disentanglement model outperforms several state-of-the-art (SOTA) methods like attribute and guided VAEs in terms of disentanglement scores. Our model distinguishes between age groups and disease status in patients with Multiple Sclerosis (MS) using the hippocampus data. Our Graph VAE with Supervised Contrastive Learning shows the volume changes of the hippocampus of MS populations at different ages, and the result is consistent with the current neuroimaging literature. This research provides valuable insights into the relationship between neurological disorder and hippocampal shape changes in different age groups of MS populations using a Graph VAE with Supervised Contrastive loss.<|reference_end|>
@article{rabbi2024disentangling,
    title={Disentangling Hippocampal Shape Variations: A Study of Neurological
  Disorders Using Mesh Variational Autoencoder with Contrastive Learning},
    author={Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu, Nilanjan Ray,
  Dana Cobzas},
    journal={Machine.Learning.for.Biomedical.Imaging. 2 (2024)},
    year={2024},
    doi={10.59275/j.melba.2024-267f},
    archivePrefix={arXiv},
    eprint={2404.00785},
    primaryClass={cs.CV cs.LG q-bio.NC}
}