Complex-valued neural networks: A comprehensive survey
CY Lee, H Hasegawa, S Gao - IEEE/CAA Journal of …, 2022 - ieeexplore.ieee.org
Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …
to their real counter-parts in speech enhancement, image and signal processing …
AI-based reconstruction for fast MRI—A systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
Adaptive diffusion priors for accelerated MRI reconstruction
Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
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 …
[HTML][HTML] FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution
High-resolution magnetic resonance images can provide fine-grained anatomical
information, but acquiring such data requires a long scanning time. In this paper, a …
information, but acquiring such data requires a long scanning time. In this paper, a …
Specificity-preserving federated learning for MR image reconstruction
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …
Image enhancement for tuberculosis detection using deep learning
The latest World Health Organization's (WHO) study on 2018 is showing that about 1.5
million people died and around 10 million people are infected with tuberculosis (TB) each …
million people died and around 10 million people are infected with tuberculosis (TB) each …
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 …
Multimodal transformer for accelerated MR imaging
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution
for fast MR imaging, providing superior performance in restoring the target modality from its …
for fast MR imaging, providing superior performance in restoring the target modality from its …
Review and prospect: artificial intelligence in advanced medical imaging
Artificial intelligence (AI) as an emerging technology is gaining momentum in medical
imaging. Recently, deep learning-based AI techniques have been actively investigated in …
imaging. Recently, deep learning-based AI techniques have been actively investigated in …