Recently, we have witnessed impressive achievements in cancer survival
analysis by integrating multimodal data, e.g., pathology images and genomic
profiles. However, the heterogeneity and high dimensionality of these
modalities pose significant challenges for extracting discriminative
representations while maintaining good generalization. In this paper, we
propose a Cohort-individual Cooperative Learning (CCL) framework to advance
cancer survival analysis by collaborating knowledge decomposition and cohort
guidance. Specifically, first, we propose a Multimodal Knowledge Decomposition
(MKD) module to explicitly decompose multimodal knowledge into four distinct
components: redundancy, synergy and uniqueness of the two modalities. Such a
comprehensive decomposition can enlighten the models to perceive easily
overlooked yet important information, facilitating an effective multimodal
fusion. Second, we propose a Cohort Guidance Modeling (CGM) to mitigate the
risk of overfitting task-irrelevant information. It can promote a more
comprehensive and robust understanding of the underlying multimodal data, while
avoiding the pitfalls of overfitting and enhancing the generalization ability
of the model. By cooperating the knowledge decomposition and cohort guidance
methods, we develop a robust multimodal survival analysis model with enhanced
discrimination and generalization abilities. Extensive experimental results on
five cancer datasets demonstrate the effectiveness of our model in integrating
multimodal data for survival analysis.

我们提出了一种合作学习框架，通过知识分解和队列引导方法协同推进癌症生存分析，以提高多模态数据的融合、鉴别和泛化能力。通过四个不同组成部分：冗余性、协同性、独特性和队列引导建模，我们的模型在融合多模态数据进行生存分析时展现出了卓越的效果。