  With the rapid advances in high-throughput sequencing technologies, the focus
of survival analysis has shifted from examining clinical indicators to
incorporating genomic profiles with pathological images. However, existing
methods either directly adopt a straightforward fusion of pathological features
and genomic profiles for survival prediction, or take genomic profiles as
guidance to integrate the features of pathological images. The former would
overlook intrinsic cross-modal correlations. The latter would discard
pathological information irrelevant to gene expression. To address these
issues, we present a Cross-Modal Translation and Alignment (CMTA) framework to
explore the intrinsic cross-modal correlations and transfer potential
complementary information. Specifically, we construct two parallel
encoder-decoder structures for multi-modal data to integrate intra-modal
information and generate cross-modal representation. Taking the generated
cross-modal representation to enhance and recalibrate intra-modal
representation can significantly improve its discrimination for comprehensive
survival analysis. To explore the intrinsic crossmodal correlations, we further
design a cross-modal attention module as the information bridge between
different modalities to perform cross-modal interactions and transfer
complementary information. Our extensive experiments on five public TCGA
datasets demonstrate that our proposed framework outperforms the
state-of-the-art methods.
