  The use of multi-modal data such as the combination of whole slide images
(WSIs) and gene expression data for survival analysis can lead to more accurate
survival predictions. Previous multi-modal survival models are not able to
efficiently excavate the intrinsic information within each modality. Moreover,
previous methods regard the information from different modalities as similarly
important so they cannot flexibly utilize the potential connection between the
modalities. To address the above problems, we propose a new asymmetrical
multi-modal method, termed as AMMASurv. Different from previous works, AMMASurv
can effectively utilize the intrinsic information within every modality and
flexibly adapts to the modalities of different importance. Encouraging
experimental results demonstrate the superiority of our method over other
state-of-the-art methods.

Computer Vision and Pattern Recognition;Artificial Intelligence