Computational health informatics in the big data age: a survey
The explosive growth and widespread accessibility of digital health data have led to a surge
of research activity in the healthcare and data sciences fields. The conventional approaches …
of research activity in the healthcare and data sciences fields. The conventional approaches …
Machine learning in electromagnetics with applications to biomedical imaging: A review
Biomedical imaging is a relevant noninvasive technique aimed at generating an image of
the biological structure under analysis. The arising visual representation of the …
the biological structure under analysis. The arising visual representation of the …
DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction
Deep learning has attracted rapidly increasing attention in the field of tomographic image
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …
Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss
The continuous development and extensive use of computed tomography (CT) in medical
practice has raised a public concern over the associated radiation dose to the patient …
practice has raised a public concern over the associated radiation dose to the patient …
Low-dose CT with a residual encoder-decoder convolutional neural network
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a
considerable interest in the medical imaging field. Currently, the main stream low-dose CT …
considerable interest in the medical imaging field. Currently, the main stream low-dose CT …
Learned primal-dual reconstruction
We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The
algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by …
algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by …
Deep convolutional neural network for inverse problems in imaging
In this paper, we propose a novel deep convolutional neural network (CNN)-based
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …
CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising
Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …
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 …