Unsupervised Domain Adaptation for Low-Dose Computed Tomography Denoising

Unsupervised Domain Adaptation for Low-Dose Computed Tomography Denoising

Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based low-dose CT denoising algorithms were primarily based on supervised learning. However, supervised learning requires a large number of training samples, which is impractical in...

Full description

Bibliographic Details
Main Authors: Jaa-Yeon Lee, Wonjin Kim, Yebin Lee, Ji-Yeon Lee, Eunji Ko, Jang-Hwan Choi
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Low-dose computed tomography (LDCT) denoising
low-dose CT
deep learning
domain adaptation
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/9969607/

Internet

https://ieeexplore.ieee.org/document/9969607/

Similar Items