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

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the
training, validation and testing of advanced deep learning (DL)-based automated tools …

Deep learning methods have recently made notable advances in the tasks of classification
and representation learning. These tasks are important for brain imaging and neuroscience …

The application of deep learning (DL) models to neuroimaging data poses several
challenges, due to the high dimensionality, low sample size, and complex temporo-spatial …

The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …

Deep learning (DL) methods have in recent years yielded impressive results in medical
imaging, with the potential to function as clinical aid to radiologists. However, DL models in …

Worldwide, there are nearly 10 million new cases of dementia annually, of which
Alzheimer's disease (AD) is the most common. New measures are needed to improve the …

Deep learning (DL) is a family of machine learning methods that has gained considerable
attention in the scientific community, breaking benchmark records in areas such as speech …

When fields lack consensus standards and ground truths for their analytic methods,
reproducibility can be more of an ideal than a reality. Such has been the case for functional …

As health data and computer power become increasingly available, the main challenge is to
gain actionable insight from these data. Machine learning (ML) methods have proved to be a …