Diffusion MRI data can be affected by hardware and subject-related artefacts that can
adversely affect downstream analyses. Therefore, automated quality control (QC) is of great …

Purpose The required training sample size for a particular machine learning (ML) model
applied to medical imaging data is often unknown. The purpose of this study was to provide …

Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …

Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

Medical imaging is an indispensable component of modern healthcare, playing a critical role
in diagnosis, staging, and the assessment of treatment response for most major medical …

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but
unfortunately suffers from long scan times which, aside from increasing operational costs …

Abstract Domain shift, the mismatch between training and testing data characteristics,
causes significant degradation in the predictive performance in multi-source imaging …

For machine learning developers, the use of prediction tools in real-world clinical settings
can be a distant goal. Recently published guidelines for reporting clinical research that …

Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …

Good quality of medical images is a prerequisite for the success of subsequent image
analysis pipelines. Quality assessment of medical images is therefore an essential activity …