With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis
and research investigating the microstructures of nervous tissues, and it has helped us to …

Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure
and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the …

Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25
years. In this review, we dwell on the various challenges along the journey of bringing a …

Deep learning (DL) has shown great potential in medical image enhancement problems,
such as super-resolution or image synthesis. However, to date, most existing approaches …

Site differences, or systematic differences in feature distributions across multiple data-
acquisition sites, are a known source of heterogeneity that may adversely affect large-scale …

MRI diffusion data suffers from significant inter-and intra-site variability, which hinders multi-
site and/or longitudinal diffusion studies. This variability may arise from a range of factors …