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

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

Background and Objectives: This study compares the clinical properties of original breast
ultrasound images and those synthesized by a generative adversarial network (GAN) to …

The task of medical image classification is increasingly supported by algorithms. Deep
learning methods like convolutional neural networks (CNNs) show superior performance in …

Aims: This work aims to explore the utilization of deep convolutional generative adversarial
networks for the synthesis of ultrasound images and to leverage its capabilities. Background …

Ultrasound imaging is cost effective, radiation-free, portable, and implemented routinely in
clinical procedures. Nonetheless, image quality is characterized by a granulated …

Breast cancer is the leading cause of death in women worldwide; prevention of possible
death from breast cancer can be decreased by early identification ultrasound image analysis …

Background To investigate the feasibility of using a stacked generative adversarial network
(sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) …

Objective. This paper proposes a conditional GAN (cGAN)-based method to perform data
enhancement of ultrasound images and segmentation of tumors in breast ultrasound …

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 …