Multi-View Variational Autoencoders Allow for Interpretability Leveraging Digital Avatars: Application to the HBN Cohort

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 5
• Main Authors: Ambroise, Corentin, Grigis, Antoine, Duchesnay, Edouard, Frouin, Vincent
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.04.2023
• Subjects: Avatars; Biomedical measurement; Brain modeling; Computational modeling; Design methodology; Mental health; Pediatrics
• ISSN: 1945-8452
• DOI: 10.1109/ISBI53787.2023.10230552

If neural network-based methods are praised for their prediction performance, they are often criticized for their lack of interpretability. When dealing with multi-omics or multi-modal data, neural network methods must be able learn the independent and joint effect of heterogeneous views while yielding interpretable results intra- and interviews. In the literature, multi-view generative models exist to learn joint information in a reduced-size latent space. Among these models, multi-view variational autoencoders are very promising. In this work, we demonstrate how they provide a convenient statistical framework to learn the input data joint distribution and offer opportunities for the results interpretation. We design a method that discovers the relationships between one view and others. The generative capabilities of the model enable the exploration of a whole disorder spectrum through the generation of realistic values. While modifying a subject's clinical score, the model retrieves a representation of the subject's brain at this clinical status, so-called digital avatar. By computing associations between cortical regions measures and behavioral scores, we showcase that such digital avatars convey interpretable information in a multi-modal cohort with children experiencing mental health issues.