Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …

Deep learning (DL) algorithms based on brain MRI images have achieved great success in
the prediction of Alzheimer's disease (AD), with classification accuracy exceeding even that …

Alzheimer's disease (AD) has become a serious hazard to human health in recent years,
and proper screening and diagnosis of AD remain a challenge. Multimodal neuroimaging …

Deep learning algorithms have a huge influence on tackling research issues in the field of
medical image processing. It acts as a vital aid for the radiologists in producing accurate …

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …

Alzheimer's disease (AD) is one of the most known causes of dementia which can be
characterized by continuous deterioration in the cognitive skills of elderly people. It is a non …

Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …

With the rapid development of various data acquisition technologies, more and more
multimodal data come into being. It is important to integrate different modalities which are …

Structural magnetic resonance imaging (sMRI) is commonly used for the identification of
Alzheimer's disease because of its keen insight into atrophy-induced changes in brain …