Deep learning-based automatic segmentation of images in cardiac radiography: a promising challenge
Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …
machine intelligence to analyze clinical image data increases the probability of disease …
Generative adversarial networks in cardiology
Generative adversarial networks (GANs) are state-of-the-art neural network models used to
synthesise images and other data. GANs brought a considerable improvement to the quality …
synthesise images and other data. GANs brought a considerable improvement to the quality …
Generative adversarial networks for image super-resolution: A survey
Single image super-resolution (SISR) has played an important role in the field of image
processing. Recent generative adversarial networks (GANs) can achieve excellent results …
processing. Recent generative adversarial networks (GANs) can achieve excellent results …
[HTML][HTML] Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time
Purpose The purpose of this study was to evaluate the impact of a deep learning-based
super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric …
super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric …
Diabetic retinopathy grading by a source-free transfer learning approach
C Zhang, T Lei, P Chen - Biomedical Signal Processing and Control, 2022 - Elsevier
Diabetic retinopathy (DR) gives rise to blindness in young adults around the world. By early
detection, patients with DR can be properly treated in time, and the deterioration of DR can …
detection, patients with DR can be properly treated in time, and the deterioration of DR can …
TransMRSR: transformer-based self-distilled generative prior for brain MRI super-resolution
Magnetic resonance images (MRI) acquired with low through-plane resolution compromise
time and cost. The poor resolution in one orientation is insufficient to meet the requirement of …
time and cost. The poor resolution in one orientation is insufficient to meet the requirement of …
RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising
M Yu, M Guo, S Zhang, Y Zhan, M Zhao… - Computers in Biology …, 2023 - Elsevier
A common problem in the field of deep-learning-based low-level vision medical images is
that most of the research is based on single task learning (STL), which is dedicated to …
that most of the research is based on single task learning (STL), which is dedicated to …
[HTML][HTML] Reconstruction and completion of high-resolution 3D cardiac shapes using anisotropic CMRI segmentations and continuous implicit neural representations
Since the onset of computer-aided diagnosis in medical imaging, voxel-based segmentation
has emerged as the primary methodology for automatic analysis of left ventricle (LV) function …
has emerged as the primary methodology for automatic analysis of left ventricle (LV) function …
Deep learning in medical image super resolution: a review
H Yang, Z Wang, X Liu, C Li, J Xin, Z Wang - Applied Intelligence, 2023 - Springer
Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …
reconstructing corresponding high-resolution (HR) images from observed low-resolution …
PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging
Background Magnetic particle imaging (MPI) is a novel tomographic imaging modality that
scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time …
scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time …