Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …

Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …

Training of a convolutional neural network (CNN) generally requires a large dataset.
However, it is not easy to collect a large medical image dataset. The purpose of this study is …

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently
exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers …

Background: Generative adversarial network (GAN)–based synthetic images can be viable
solutions to current supervised deep learning challenges. However, generating highly …

In recent years, deep learning techniques have been applied in musculoskeletal radiology
to increase the diagnostic potential of acquired images. Generative adversarial networks …

Background Deep learning methods have been shown to be useful for segmentation of
lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on …

Objective To develop a precise semi-automated segmentation of the fascia lata (FL) of the
thigh to quantify IMAT volume in T 1 w MR images and fat fraction (FF) in Dixon MR images …

The accurate diagnosis of neuromuscular diseases (NMD) is in many cases difficult; the
starting point is the clinical approach based on the course of the disease and a careful …