Deep convolutional generative adversarial networks (DCGANs) are newly developed tools
for generating synthesized images. To determine the clinical utility of synthesized images …

Objectives We sought to generate realistic synthetic breast ultrasound images and express
virtual interpolation images of tumors using a deep convolutional generative adversarial …

Deep learning (DL) models are becoming pervasive and applicable to computer vision,
image processing, and synthesis problems. The performance of these models is often …

The ability to generate synthetic medical images is useful for data augmentation, domain
transfer, and out-of-distribution detection. However, generating realistic, high-resolution …

The convolutional neural network (CNN) is a promising technique to detect breast cancer
based on mammograms. Training the CNN from scratch, however, requires a large amount …

Purpose To explore whether generative adversarial networks (GANs) can enable synthesis
of realistic medical images that are indiscernible from real images, even by domain experts …

Abstract Background and Objective The capability of deep learning radiomics (DLR) to
extract high-level medical imaging features has promoted the use of computer-aided …

Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical
diagnosis. The training of new sonographers and deep learning based algorithms for US …

Deep Learning is vital in medical imaging solutions and clinical applications. However,
multiple reasons, such as data scarcity and imbalance in the medical image dataset, cause …

Plane wave ultrasound imaging is an ideal approach to achieve maximum real-time frame
rates. However, multiple plane wave insonifications at different angles are often combined to …