[HTML][HTML] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
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 …
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 …
Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
[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 …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
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 …
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …
Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network
Optical coherence tomography (OCT) has become a very promising diagnostic method in
clinical practice, especially for ophthalmic diseases. However, speckle noise and low …
clinical practice, especially for ophthalmic diseases. However, speckle noise and low …
[HTML][HTML] 深度学习在医学影像中的应用综述
施俊， 汪琳琳， 王珊珊， 陈艳霞， 王乾， 魏冬铭， 梁淑君… - 2020 - cjig.cn
摘要深度学习能自动从大样本数据中学习获得优良的特征表达, 有效提升各种机器学习任务的
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …