Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding
factor concerning brain research as well as clinical practice. For this reason, several …

Containing the medical data of millions of patients, clinical data warehouses (CDWs)
represent a great opportunity to develop computational tools. Magnetic resonance images …

The efficacy of content-based image classification is dependent on the richness of the
feature vectors extracted from the image data. Traditional feature extraction techniques …

Clinical data warehouses (CDWs) contain the medical data of millions of patients and
represent a great opportunity to develop computational tools. MRIs are particularly sensitive …

Abstract Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI)
is a field of study that aims to automatically flag motion artefacts in order to prevent the …

MRI image segmentation is widely used in clinical practice as a prerequisite and a key for
diagnosing brain tumors. The quest for an accurate automated segmentation method for …

Introduction: Machine learning methods combined with satellite imagery have the potential
to improve estimates of carbon uptake of terrestrial ecosystems, including croplands …

Quality assessment, including inspecting the images for artifacts, is a critical step during MRI
data acquisition to ensure data quality and downstream analysis or interpretation success …

Brain dynamics are highly complex and yet hold the key to understanding brain function and
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …

The recent advent of clinical data warehouses (CDWs) has facilitated the sharing of very
large volumes of medical data for research purposes. MRIs can be affected by various …