Representation Disentanglement for Multi-modal Brain MRI Analysis.

ABSTRACT: Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested that multi-modal deep learning analysis can benefit from explicitly disentangling anatomical (shape) and modality (appearance) information into separate image presentations. In this work, we challenge mainstream strategies by showing that they do not naturally lead to representation disentanglement both in theory and in practice. To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities. To enable robust training, we further use a conditional convolution to design a single model for encoding images of all modalities. Lastly,

SUBMITTER: Ouyang J 

PROVIDER: S-EPMC8844656 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature