Medical image super-resolution reconstruction algorithms based on deep learning: A survey
D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
[HTML][HTML] A review and experimental evaluation of deep learning methods for MRI reconstruction
Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …
based machine-learning techniques have received significant interest for accelerating …
Hierarchical perception adversarial learning framework for compressed sensing MRI
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI)
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …
Updates in deep learning research in ophthalmology
WY Ng, S Zhang, Z Wang, CJT Ong… - Clinical …, 2021 - portlandpress.com
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the
medical field. Deep learning (DL), in particular, has garnered significant attention due to the …
medical field. Deep learning (DL), in particular, has garnered significant attention due to the …
Gan-tl: Generative adversarial networks with transfer learning for mri reconstruction
Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient
technique for image reconstruction using under-sampled MR data. In most cases, the …
technique for image reconstruction using under-sampled MR data. In most cases, the …
Adaptive channel-modulated personalized federated learning for magnetic resonance image reconstruction
Magnetic resonance imaging (MRI) is extensively utilized in clinical practice for diagnostic
purposes, owing to its non-invasive nature and remarkable ability to provide detailed …
purposes, owing to its non-invasive nature and remarkable ability to provide detailed …
A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges
M Megahed, A Mohammed - Wiley Interdisciplinary Reviews …, 2024 - Wiley Online Library
In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
PUERT: Probabilistic under-sampling and explicable reconstruction network for CS-MRI
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-
Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues …
Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues …
Fast MRI reconstruction: How powerful transformers are?
Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method
for clinical interro-gation of organ structures and metabolism, with an inherently long …
for clinical interro-gation of organ structures and metabolism, with an inherently long …