Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …

Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …

Purpose To describe a large, publicly available dataset comprising computed tomography
(CT) projection data from patient exams, both at routine clinical doses and simulated lower …

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …

The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …