A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
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 …
Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …
recognition is a challenging problem for radiologists in health monitoring and automated …
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …
Image reconstruction is a new frontier of machine learning
Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …
generated overwhelming research interest and attracted unprecedented public attention. As …
DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising
Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …
SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network
Computed tomography (CT) is a widely used screening and diagnostic tool that allows
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …
Low‐dose CT image and projection dataset
Purpose To describe a large, publicly available dataset comprising computed tomography
(CT) projection data from patient exams, both at routine clinical doses and simulated lower …
(CT) projection data from patient exams, both at routine clinical doses and simulated lower …
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 …