  Multimodal neuroimage can provide complementary information about the
dementia, but small size of complete multimodal data limits the ability in
representation learning. Moreover, the data distribution inconsistency from
different modalities may lead to ineffective fusion, which fails to
sufficiently explore the intra-modal and inter-modal interactions and
compromises the disease diagnosis performance. To solve these problems, we
proposed a novel multimodal representation learning and adversarial hypergraph
fusion (MRL-AHF) framework for Alzheimer's disease diagnosis using complete
trimodal images. First, adversarial strategy and pre-trained model are
incorporated into the MRL to extract latent representations from multimodal
data. Then two hypergraphs are constructed from the latent representations and
the adversarial network based on graph convolution is employed to narrow the
distribution difference of hyperedge features. Finally, the hyperedge-invariant
features are fused for disease prediction by hyperedge convolution. Experiments
on the public Alzheimer's Disease Neuroimaging Initiative(ADNI) database
demonstrate that our model achieves superior performance on Alzheimer's disease
detection compared with other related models and provides a possible way to
understand the underlying mechanisms of disorder's progression by analyzing the
abnormal brain connections.
