The Role of generative adversarial network in medical image analysis: An in-depth survey
M AlAmir, M AlGhamdi - ACM Computing Surveys, 2022 - dl.acm.org
A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …
the field of artificial intelligence, and its superior data generation capability has garnered …
[HTML][HTML] Prospects of structural similarity index for medical image analysis
An image quality matrix provides a significant principle for objectively observing an image
based on an alteration between the original and distorted images. During the past two …
based on an alteration between the original and distorted images. During the past two …
Fine perceptive GANs for brain MR image super-resolution in wavelet domain
Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration.
However, limited by factors such as imaging hardware, scanning time, and cost, it is …
However, limited by factors such as imaging hardware, scanning time, and cost, it is …
CuNeRF: Cube-based neural radiance field for zero-shot medical image arbitrary-scale super resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread
attention, aiming to supersample medical volumes at arbitrary scales via a single model …
attention, aiming to supersample medical volumes at arbitrary scales via a single model …
Memory-augmented deep unfolding network for guided image super-resolution
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …
[HTML][HTML] SOUP-GAN: Super-resolution MRI using generative adversarial networks
There is a growing demand for high-resolution (HR) medical images for both clinical and
research applications. Image quality is inevitably traded off with acquisition time, which in …
research applications. Image quality is inevitably traded off with acquisition time, which in …
[HTML][HTML] Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning
Generative adversarial networks (GAN) can produce images of improved quality but their
ability to augment image-based classification is not fully explored. We evaluated if a …
ability to augment image-based classification is not fully explored. We evaluated if a …
Multicontrast mri super-resolution via transformer-empowered multiscale contextual matching and aggregation
Magnetic resonance imaging (MRI) possesses the unique versatility to acquire images
under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution …
under a diverse array of distinct tissue contrasts, which makes multicontrast super-resolution …
[HTML][HTML] Super-resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning
Y Xia, N Ravikumar, JP Greenwood, S Neubauer… - Medical Image …, 2021 - Elsevier
Abstract High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is
challenging since it requires long acquisition and patient breath-hold times. Instead, 2D …
challenging since it requires long acquisition and patient breath-hold times. Instead, 2D …
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 …