Over the past decade, machine learning (ML) and artificial intelligence (AI) have become
increasingly prevalent in the medical field. In the United States, the Food and Drug …

The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise
brain morphological measurements; however, scanner and/or protocol variations may …

Background The growth in multi-center neuroimaging studies generated a need for methods
that mitigate the differences in hardware and acquisition protocols across sites ie, scanner …

Background and purpose Data from neuro‐imaging techniques allow us to estimate a
brain's age. Brain age is easily interpretable as 'how old the brain looks' and could therefore …

Abstract Domain adaptation (DA) is an important technique for modern machine learning-
based medical data analysis, which aims at reducing distribution differences between …

Purpose Volume measurement using MRI is important to assess brain atrophy in multiple
sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis …

Background Epileptic seizures are an established comorbidity of Alzheimer's disease (AD).
Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or …

Purpose Two-dimensional single-slice abdominal computed tomography (CT) provides a
detailed tissue map with high resolution allowing quantitative characterization of …

Developmental dyslexia (DD) is defined as difficulties in learning to read even with normal
intelligence and adequate educational guidance. Deficits in implicit sequence learning (ISL) …

Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this
study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae …