QS-ADN: quasi-supervised artifact disentanglement network for low-dose CT image denoising by local similarity among unpaired data.

Ruan, Yuhui; Yuan, Qiao; Niu, Chuang; Li, Chen; Yao, Yudong; Wang, Ge; Teng, Yueyang

Ruan, Yuhui; Yuan, Qiao; Niu, Chuang; Li, Chen; Yao, Yudong; Wang, Ge; Teng, Yueyang.

Afiliación

Ruan Y; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, People's Republic of China.
Yuan Q; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, People's Republic of China.
Niu C; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America.
Li C; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, People's Republic of China.
Yao Y; Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030 United States of America.
Wang G; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America.
Teng Y; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China, and with the Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110169, People's Republic of China.

Phys Med Biol ; 68(20)2023 Oct 02.

Article en En | MEDLINE | ID: mdl-37708896

Palabras clave

CT image denoising; deep learning; local similarity; quasi-supervised learning; unpaired data

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Med Biol Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido

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