Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

We study on image super-resolution (SR), which aims to recover realistic textures from a low-
resolution (LR) image. Recent progress has been made by taking high-resolution images as …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …

Image-to-image translation is considered a new frontier in the field of medical image
analysis, with numerous potential applications. However, a large portion of recent …

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Abstract Background and Objective High-resolution (HR) MR images provide rich structural
detail to assist physicians in clinical diagnosis and treatment plan. However, it is arduous to …

Cardiovascular imaging is going to change substantially in the next decade, fueled by the
deep learning revolution. For medical professionals, it is important to keep track of these …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …