Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with
cognitive decline. Memory problems are typically among the first signs of cognitive …

As medical imaging enters its information era and presents rapidly increasing needs for big
data analytics, robust pooling and harmonization of imaging data across diverse cohorts …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Abstract Machine learning algorithms can be trained to estimate age from brain structural
MRI. The difference between an individual's predicted and chronological age, predicted age …

With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …

The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …

The brain changes as we age and these changes are associated with functional
deterioration and neurodegenerative disease. It is vital that we better understand individual …

Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …

The brain-age paradigm is proving increasingly useful for exploring aging-related disease
and can predict important future health outcomes. Most brain-age research uses structural …