Deep learning on image denoising: An overview
Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …
However, there are substantial differences in the various types of deep learning methods …
The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
Deep learning for biomedical image reconstruction: A survey
Medical imaging is an invaluable resource in medicine as it enables to peer inside the
human body and provides scientists and physicians with a wealth of information …
human body and provides scientists and physicians with a wealth of information …
XctNet: Reconstruction network of volumetric images from a single X-ray image
Abstract Conventional Computed Tomography (CT) produces volumetric images by
computing inverse Radon transformation using X-ray projections from different angles …
computing inverse Radon transformation using X-ray projections from different angles …
Self-supervised deep learning for joint 3D low-dose PET/CT image denoising
F Zhao, D Li, R Luo, M Liu, X Jiang, J Hu - Computers in Biology and …, 2023 - Elsevier
Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET)
and low-dose computed tomography (LDCT) has been widely explored. However, previous …
and low-dose computed tomography (LDCT) has been widely explored. However, previous …
Collaborative learning classification model for PCBs defect detection against image and label uncertainty
X Yu, L Han-Xiong, H Yang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Surface defect detection of printed circuit boards (PCBs) is a critical stage in ensuring
product quality on production lines in electronics manufacturing. The excellent performance …
product quality on production lines in electronics manufacturing. The excellent performance …
Cross-domain unpaired learning for low-dose ct imaging
Supervised deep-learning techniques with paired training datasets have been widely
studied for low-dose computed tomography (LDCT) imaging with excellent performance …
studied for low-dose computed tomography (LDCT) imaging with excellent performance …
MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks
Low-dose CT techniques attempt to minimize the radiation exposure of patients by
estimating the high-resolution normal-dose CT images to reduce the risk of radiation …
estimating the high-resolution normal-dose CT images to reduce the risk of radiation …
Dual residual convolutional neural network (DRCNN) for low-dose CT imaging
Z Feng, A Cai, Y Wang, L Li, L Tong… - Journal of X-Ray …, 2021 - content.iospress.com
The excessive radiation doses in the application of computed tomography (CT) technology
pose a threat to the health of patients. However, applying a low radiation dose in CT can …
pose a threat to the health of patients. However, applying a low radiation dose in CT can …
Learning a deep CNN denoising approach using anatomical prior information implemented with attention mechanism for low-dose CT imaging on clinical patient data …
Z Huang, X Liu, R Wang, Z Chen… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Dose reduction in computed tomography (CT) has gained considerable attention in clinical
applications because it decreases radiation risks. However, a lower dose generates noise in …
applications because it decreases radiation risks. However, a lower dose generates noise in …