Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
GANs for medical image analysis
Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …
exciting ways to tackle well known and challenging medical image analysis problems such …
Medical image generation using generative adversarial networks: A review
Generative adversarial networks (GANs) are unsupervised deep learning approach in the
computer vision community which has gained significant attention from the last few years in …
computer vision community which has gained significant attention from the last few years in …
MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI
Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that
can accurately reconstruct images from undersampled k-space data with a much lower …
can accurately reconstruct images from undersampled k-space data with a much lower …
Deep learning in the biomedical applications: Recent and future status
Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination
Purpose To systematically investigate the influence of various data consistency layers and
regularization networks with respect to variations in the training and test data domain, for …
regularization networks with respect to variations in the training and test data domain, for …
A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI
A Bahrami, A Karimian, E Fatemizadeh… - Medical …, 2020 - Wiley Online Library
Purpose Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in
radiation therapy, MRI‐guided radiation treatment planning is limited by the fact that MRI …
radiation therapy, MRI‐guided radiation treatment planning is limited by the fact that MRI …
Unpaired deep learning for accelerated MRI using optimal transport driven CycleGAN
Recently, deep learning approaches for accelerated MRI have been extensively studied
thanks to their high performance reconstruction in spite of significantly reduced run-time …
thanks to their high performance reconstruction in spite of significantly reduced run-time …
Reducing uncertainty in undersampled MRI reconstruction with active acquisition
The goal of MRI reconstruction is to restore a high fidelity image from partially observed
measurements. This partial view naturally induces reconstruction uncertainty that can only …
measurements. This partial view naturally induces reconstruction uncertainty that can only …
Deep-learning-based multi-modal fusion for fast MR reconstruction
T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired
magnetic resonance (MR) modalities that can provide complementary information for clinical …
magnetic resonance (MR) modalities that can provide complementary information for clinical …