Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes by …

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was
set up to analyze brain measures and genotypes from multiple sites across the world to …

Diffusion tensor imaging (DTI) is used to study tissue composition and architecture in vivo.
To increase the signal to noise ratio (SNR) of DTI contrasts, studies typically use more than …

International challenges have become the de facto standard for comparative assessment of
image analysis algorithms. Although segmentation is the most widely investigated medical …

Vision Transformers (ViT) s have shown great performance in self-supervised learning of
global and local representations that can be transferred to downstream applications. Inspired …

Semantic segmentation of medical images aims to associate a pixel with a label in a medical
image without human initialization. The success of semantic segmentation algorithms is …

Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have
shown prominence for the majority of medical image segmentation applications since the past …

Chemical exchange saturation transfer (CEST) is a contrast mechanism that exploits
exchange‐based magnetization transfer (MT) between solute and water protons. CEST effects …

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a
collaborative network of researchers working together on a range of large-scale studies that …

Purpose To develop an experimental protocol to calculate the precision and accuracy of
fractional anisotropy (FA), mean diffusivity (MD), and the orientation of the principal eigenvector (…