Multimodal Survival Ensemble Network: Integrating Genomic and Histopathological Insights for Enhanced Cancer Prognosis

Abstract

Cancer’s inherent heterogeneity demands a multimodal approach to provide an accurate prognosis, taking into account histological, clinical, and genomic data. As the field of artificial intelligence evolves with advancements in multimodal learning, its role in survival analysis becomes increasingly critical. We introduce the Multimodal Survival Ensemble Network (MSEN), a novel weakly-supervised framework designed for the seamless integration of genomic data and histopathological images. Not only does our method preserve the heterogeneity among different genomic modalities during integration, but it also ensures superior retention of spatial information in histopathological images compared to traditional techniques. Rigorous evaluations across five datasets highlight MSEN’s superior performance, marking a progressive step in cancer prognosis.