Recent Advances in Deep Multimodal Fusion in Comput-er-Aided Diagnosis Systems: A literature Review

Abstract

Deep learning-based approaches have demonstrated great results; in handling the complexities of multimodal data and learning informative repre-sentations from heterogeneous modalities, these multimodal fusion techniques have attracted considerable attention for their role in the integration of infor-mation from different data modalities. In computer aided diagnosis (CAD) sys-tems, the mixture of different information extracted from heterogeneous modal-ities, like medical images, clinical data, genetic data, or textual reports, can pro-vide a more comprehensive and reliable assessment of diseases or conditions. This review article examines advances in deep multimodal fusion using hetero- geneous neural networks for medical computer-aided-diagnosis (CAD) systems.