  Morphological attributes from histopathological images and molecular profiles
from genomic data are important information to drive diagnosis, prognosis, and
therapy of cancers. By integrating these heterogeneous but complementary data,
many multi-modal methods are proposed to study the complex mechanisms of
cancers, and most of them achieve comparable or better results from previous
single-modal methods. However, these multi-modal methods are restricted to a
single task (e.g., survival analysis or grade classification), and thus neglect
the correlation between different tasks. In this study, we present a
multi-modal fusion framework based on multi-task correlation learning
(MultiCoFusion) for survival analysis and cancer grade classification, which
combines the power of multiple modalities and multiple tasks. Specifically, a
pre-trained ResNet-152 and a sparse graph convolutional network (SGCN) are used
to learn the representations of histopathological images and mRNA expression
data respectively. Then these representations are fused by a fully connected
neural network (FCNN), which is also a multi-task shared network. Finally, the
results of survival analysis and cancer grade classification output
simultaneously. The framework is trained by an alternate scheme. We
systematically evaluate our framework using glioma datasets from The Cancer
Genome Atlas (TCGA). Results demonstrate that MultiCoFusion learns better
representations than traditional feature extraction methods. With the help of
multi-task alternating learning, even simple multi-modal concatenation can
achieve better performance than other deep learning and traditional methods.
Multi-task learning can improve the performance of multiple tasks not just one
of them, and it is effective in both single-modal and multi-modal data.
