  Survival prediction, utilizing pathological images and genomic profiles, is
increasingly important in cancer analysis and prognosis. Despite significant
progress, precise survival analysis still faces two main challenges: (1) The
massive pixels contained in whole slide images (WSIs) complicate the process of
pathological images, making it difficult to generate an effective
representation of the tumor microenvironment (TME). (2) Existing multimodal
methods often rely on alignment strategies to integrate complementary
information, which may lead to information loss due to the inherent
heterogeneity between pathology and genes. In this paper, we propose a
Multimodal Cross-Task Interaction (MCTI) framework to explore the intrinsic
correlations between subtype classification and survival analysis tasks.
Specifically, to capture TME-related features in WSIs, we leverage the subtype
classification task to mine tumor regions. Simultaneously, multi-head attention
mechanisms are applied in genomic feature extraction, adaptively performing
genes grouping to obtain task-related genomic embedding. With the joint
representation of pathological images and genomic data, we further introduce a
Transport-Guided Attention (TGA) module that uses optimal transport theory to
model the correlation between subtype classification and survival analysis
tasks, effectively transferring potential information. Extensive experiments
demonstrate the superiority of our approaches, with MCTI outperforming
state-of-the-art frameworks on three public benchmarks.
\href{https://github.com/jsh0792/MCTI}{https://github.com/jsh0792/MCTI}.
