Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

Since the second half of the twentieth century, intracranial electroencephalography (iEEG),
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …

With the potential development of new disease-modifying Alzheimer's disease (AD)
therapies, simple, widely available screening tests are needed to identify which individuals …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in
hospitals across the world. These have the potential to revolutionize our understanding of …

Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …

Objective The glymphatic system cleans amyloid and tau proteins from the brain in animal
studies of Alzheimer disease (AD). However, there is no direct evidence showing this in …

Data diversity is critical to success when training deep learning models. Medical imaging
data sets are often imbalanced as pathologic findings are generally rare, which introduces …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials.
In this study, we have developed a new approach based on 3D deep convolutional neural …