CT image denoising and deblurring with deep learning: current status and perspectives
This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
and deblurring separately and simultaneously. Then, we discuss promising directions in this …
Inter-slice consistency for unpaired low-dose CT denoising using boosted contrastive learning
The research field of low-dose computed tomography (LDCT) denoising is primarily
dominated by supervised learning-based approaches, which necessitate the accurate …
dominated by supervised learning-based approaches, which necessitate the accurate …
Re-UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction
L Xiong, N Li, W Qiu, Y Luo, Y Li, Y Zhang - Medical & Biological …, 2024 - Springer
In recent years, the growing awareness of public health has brought attention to low-dose
computed tomography (LDCT) scans. However, the CT image generated in this way …
computed tomography (LDCT) scans. However, the CT image generated in this way …
Multi-Scale Texture Loss for CT denoising with GANs
Generative Adversarial Networks (GANs) have proved as a powerful framework for
denoising applications in medical imaging. However, GAN-based denoising algorithms still …
denoising applications in medical imaging. However, GAN-based denoising algorithms still …
Medical Image Denoising and Brain Tumor Detection Using CNN and U-Net
BD Shivahare, SK Gupta, AN Katiyar… - 2023 3rd …, 2023 - ieeexplore.ieee.org
The process of medical imaging, which incorporates images produced by X-rays, ultrasound
imaging, angiography, etc., is intricate. During the imaging process, image noise is also …
imaging, angiography, etc., is intricate. During the imaging process, image noise is also …
FedFDD: Federated Learning with Frequency Domain Decomposition for Low-Dose CT Denoising
Low-dose computed tomography (LDCT) enables imaging with minimal radiation exposure
but typically results in noisy outputs. Deep learning algorithms have been emerging as …
but typically results in noisy outputs. Deep learning algorithms have been emerging as …
A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising
J Chi, Z Sun, S Tian, H Wang, S Wang - Journal of Imaging Informatics in …, 2024 - Springer
Low-dose computer tomography (LDCT) has been widely used in medical diagnosis.
Various denoising methods have been presented to remove noise in LDCT scans. However …
Various denoising methods have been presented to remove noise in LDCT scans. However …
Automatic feature extraction by supervised and contrastive self-supervised learning based on wavelet and hard negatives to detect HIFU lesion area
M Zavar, HR Ghaffary, H Tabatabaee - 2023 - researchsquare.com
Abstract The adoption of Deep Neural Networks has surged due to their ability to
automatically extract features and employ diverse approaches in data analysis. This …
automatically extract features and employ diverse approaches in data analysis. This …