The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging.
Low power requirements and transportability have enabled scanning outside the controlled …

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-based segmentation methods provide an effective and automated way for
assessing the structure and function of the heart in cardiac magnetic resonance (CMR) …

Automatic segmentation of ground glass opacities and consolidations in chest computer
tomography (CT) scans can potentially ease the burden of radiologists during times of high …

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

Blanking plays an important role in most manufacturing chains as this forming operation
defines the final geometry as well as functional properties of the product. As a result of high …

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder
associated with age that affects motor and cognitive functions. As there is currently no cure …

Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …

Data-driven deep learning has been considered a promising method for building powerful
models for medical data, which often requires a large amount of diverse data to be …

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin
with automated tumor detection, which can provide the foundation for downstream AI tasks …