Real-world single image super-resolution: A brief review
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 …
image from a low-resolution (LR) observation, has been an active research topic in the area …
Deep learning for cardiac image segmentation: a review
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 …
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
Learning texture transformer network for image super-resolution
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 …
resolution (LR) image. Recent progress has been made by taking high-resolution images as …
Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
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 …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
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 …
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
A review of the deep learning methods for medical images super resolution problems
Y Li, B Sixou, F Peyrin - Irbm, 2021 - Elsevier
Super resolution problems are widely discussed in medical imaging. Spatial resolution of
medical images are not sufficient due to the constraints such as image acquisition time, low …
medical images are not sufficient due to the constraints such as image acquisition time, low …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
MedGAN: Medical image translation using GANs
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 …
analysis, with numerous potential applications. However, a large portion of recent …
State-of-the-art deep learning in cardiovascular image analysis
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 revolution. For medical professionals, it is important to keep track of these …
Task transformer network for joint MRI reconstruction and super-resolution
The core problem of Magnetic Resonance Imaging (MRI) is the trade off between
acceleration and image quality. Image reconstruction and super-resolution are two crucial …
acceleration and image quality. Image reconstruction and super-resolution are two crucial …