Multimodal neuroimage can provide complementary information about the dementia, but
small size of complete multimodal data limits the ability in representation learning. Moreover …

Multimodal neuroimaging provides complementary information critical for accurate early
diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal …

Early diagnosis of Alzheimer's disease (AD) plays a key role in preventing and responding
to this neurodegenerative disease. It has shown that, compared with a single imaging …

Using multimodal neuroimaging data to characterize brain network is currently an advanced
technique for Alzheimer's disease (AD) Analysis. Over recent years the neuroimaging …

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and
functional connectivity during its progressive degenerative processes. Existing auxiliary …

Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of
dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive …

Background Compared with single-modal neuroimages classification of AD, multi-modal
classification can achieve better performance by fusing different information. Exploring …

Fusing structural-functional images of the brain has shown great potential to analyze the
deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse …

The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great
relevance for patient treatment to improve quality of life. We address this problem as a multi …

Effective fusion of multi-modality neuroimaging data, such as structural magnetic resonance
imaging (MRI) and fluorodeoxyglucose positron emission tomography (PET), has attracted …