A multi-modal fusion model with enhanced feature representation for chronic kidney disease progression prediction.

ABSTRACT: Artificial intelligence (AI)-based multi-modal fusion algorithms are pivotal in emulating clinical practice by integrating data from diverse sources. However, most of the existing multi-modal models focus on designing new modal fusion methods, ignoring critical role of feature representation. Enhancing feature representativeness can address the noise caused by modal heterogeneity at the source, enabling high performance even with small datasets and simple architectures. Here, we introduce DeepOmix-FLEX (Fusion with Learning Enhanced feature representation for X-modal or FLEX in short), a multi-modal fusion model that integrates clinical data, proteomic data, metabolomic data, and pathology images across different scales and modalities, with a focus on advanced feature learning and represen

SUBMITTER: Qiao Y 

PROVIDER: S-EPMC11801269 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature