Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis

arXiv:2404.02394v1 Announce Type: cross
Abstract: 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 …

abstract analysis arxiv cancer challenges cs.cv data dimensionality eess.iv genomic good however images multimodal multimodal data paper pathology profiles survival type