Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide
range of healthcare applications, from medical image computing and analysis to continuous …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Recently, deep learning has been demonstrated to be feasible in eliminating the use of
gadoliniumbased contrast agents (GBCAs) through synthesizing gadolinium-free contrast …

New lesion segmentation is essential to estimate the disease progression and therapeutic
effects during multiple sclerosis (MS) clinical treatments. However, the expensive data …

Combining large multi-center datasets can enhance statistical power, particularly in the field
of neurology, where data can be scarce. However, applying a deep learning model trained …

Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early
neurodevelopment. However, segmenting small regions such as limbic structures is …

The image acquisition parameters (IAPs) used to create MRI scans are central to defining
the appearance of the images. Deep learning models trained on data acquired using certain …

Alzheimer's disease (AD) is the most common form of dementia and there is no effective
treatment currently. Using artificial intelligence technology to assist the diagnosis and …

Federated domain adaptation (FDA) describes the setting where a set of source clients seek
to optimize the performance of a target client. To be effective, FDA must address some of the …

In this study, we aimed at investigating generalizability of GFCE-MRI model using data from
seven institutions by manipulating heterogeneity of training MRI data under two popular …