  Effective understanding of a disease such as cancer requires fusing multiple
sources of information captured across physical scales by multimodal data. In
this work, we propose a novel feature embedding module that derives from
canonical correlation analyses to account for intra-modality and inter-modality
correlations. Experiments on simulated and real data demonstrate how our
proposed module can learn well-correlated multi-dimensional embeddings. These
embeddings perform competitively on one-year survival classification of
TCGA-BRCA breast cancer patients, yielding average F1 scores up to 58.69% under
5-fold cross-validation.
