Cddnet: Cross-domain denoising network for low-dose ct image via local and global information alignment
The domain shift problem has emerged as a challenge in cross-domain low-dose CT
(LDCT) image denoising task, where the acquisition of a sufficient number of medical …
(LDCT) image denoising task, where the acquisition of a sufficient number of medical …
Deep embedding-attention-refinement for sparse-view CT reconstruction
Tomographic image reconstruction with deep learning is an emerging field of applied
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
artificial intelligence. Reducing radiation dose with sparse views' reconstruction is a …
CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …
starvation and electronic noise. Recently, some works have attempted to use diffusion …
Low-dose CT image synthesis for domain adaptation imaging using a generative adversarial network with noise encoding transfer learning
M Li, J Wang, Y Chen, Y Tang, Z Wu… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Deep learning (DL) based image processing methods have been successfully applied to
low-dose x-ray images based on the assumption that the feature distribution of the training …
low-dose x-ray images based on the assumption that the feature distribution of the training …
Sparse Bayesian Deep Learning for Cross Domain Medical Image Reconstruction
Cross domain medical image reconstruction aims to address the issue that deep learning
models trained solely on one source dataset might not generalize effectively to unseen …
models trained solely on one source dataset might not generalize effectively to unseen …
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 …
Structure-preserved meta-learning uniting network for improving low-dose CT quality
Objective. Deep neural network (DNN) based methods have shown promising performances
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …
for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based …
Federated Condition Generalization on Low-dose CT Reconstruction via Cross-domain Learning
The harmful radiation dose associated with CT imaging is a major concern because it can
cause genetic diseases. Acquiring CT data at low radiation doses has become a pressing …
cause genetic diseases. Acquiring CT data at low radiation doses has become a pressing …
[HTML][HTML] Parallel processing model for low-dose computed tomography image denoising
L Yao, J Wang, Z Wu, Q Du, X Yang, M Li… - Visual Computing for …, 2024 - Springer
Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial
role in reducing radiation exposure in patients. However, LDCT-reconstructed images often …
role in reducing radiation exposure in patients. However, LDCT-reconstructed images often …
Cross-domain Low-dose CT Image Denoising with Semantic Preservation and Noise Alignment
Deep learning (DL)-based Low-dose CT (LDCT) image denoising methods may face
domain shift problem, where data from different domains (ie, hospitals) may have similar …
domain shift problem, where data from different domains (ie, hospitals) may have similar …