Multimodal Survival

Multimodal survival analysis aims to predict patient survival time by integrating diverse data types, such as genomic profiles and pathology images, to improve cancer prognosis and treatment stratification. Current research focuses on developing robust deep learning models, including transformer networks, heterogeneous graph networks, and attention-based architectures, that effectively fuse these heterogeneous data sources while addressing challenges like high dimensionality, missing data, and interpretability. These advancements offer the potential for more accurate and personalized cancer care by providing clinicians with more precise risk assessments and facilitating the development of targeted therapies.