A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
Applications of a deep learning method for anti-aliasing and super-resolution in MRI
Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios
(SNRs) are desired in many clinical and research applications. However, acquiring such …
(SNRs) are desired in many clinical and research applications. However, acquiring such …
Artificial intelligence in multiparametric magnetic resonance imaging: A review
Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …
Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network
High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical
information important for clinical application and quantitative image analysis. However, HR …
information important for clinical application and quantitative image analysis. However, HR …
Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution
Magnetic resonance imaging (MRI) can present multi-contrast images of the same
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …
Multiscale brain MRI super-resolution using deep 3D convolutional networks
CH Pham, C Tor-Díez, H Meunier, N Bednarek… - … Medical Imaging and …, 2019 - Elsevier
The purpose of super-resolution approaches is to overcome the hardware limitations and
the clinical requirements of imaging procedures by reconstructing high-resolution images …
the clinical requirements of imaging procedures by reconstructing high-resolution images …
On the applications of robust PCA in image and video processing
Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …
matrices offers a powerful framework for a large variety of applications such as image …
Brain MRI super resolution using 3D deep densely connected neural networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical
information and is often necessary for accurate quantitative analysis. However, high spatial …
information and is often necessary for accurate quantitative analysis. However, high spatial …
SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning
High resolution magnetic resonance (MR) images are desired in many clinical and research
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
[ΒΙΒΛΙΟ][B] Deep learning for medical image analysis
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for
academic and industry researchers and graduate students taking courses on machine …
academic and industry researchers and graduate students taking courses on machine …