Disentanglement and codebook learning-induced feature match network to diagnose neurodegenerative diseases on incomplete multimodal data

Abstract

Multimodal data can provide complementary information to diagnose neurodegenerative diseases (NDs). However, image quality variations and high costs can result in the missing data problem. Although incomplete multimodal data can be projected onto a common space, the traditional projection process may increase alignment errors and lose some modality-specific information. A disentanglement and codebook learning-induced feature match network (DCFMnet) is proposed in this study to solve the aforementioned issues. First, multimodal data are disentangled into latent modality-common and -specific features to help preserve modality-specific information in the subsequent alignment of multimodal data. Second, the latent modal features of all available data are aligned into a common space to reduce alignment errors and fused to achieve ND diagnosis. Moreover, the latent modal features of the modality with missing data are explored in online updated feature codebooks. Last, DCFMnet is tested on two publicly available datasets to illustrate its excellent performance in ND diagnosis.