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...

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Autors principals:	Jaa-Yeon Lee, Wonjin Kim, Yebin Lee, Ji-Yeon Lee, Eunji Ko, Jang-Hwan Choi
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2022-01-01
Col·lecció:	IEEE Access
Matèries:	Low-dose computed tomography (LDCT) denoising low-dose CT deep learning domain adaptation unsupervised learning
Accés en línia:	https://ieeexplore.ieee.org/document/9969607/
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