Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

Jiang, Gongfa; He, Zilong; Zhou, Yuanpin; Wei, Jun; Xu, Yuesheng; Zeng, Hui; Wu, Jiefang; Qin, Genggeng; Chen, Weiguo; Lu, Yao

Jiang, Gongfa; He, Zilong; Zhou, Yuanpin; Wei, Jun; Xu, Yuesheng; Zeng, Hui; Wu, Jiefang; Qin, Genggeng; Chen, Weiguo; Lu, Yao.

Affiliation

Jiang G; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China.
He Z; Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China.
Zhou Y; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China.
Wei J; Perception Vision Medical Technology Company Ltd., Guangzhou, P. R. China.
Xu Y; Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
Zeng H; Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA.
Wu J; Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China.
Qin G; Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China.
Chen W; Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China.
Lu Y; Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China.

Med Phys ; 50(2): 837-853, 2023 Feb.

Article in En | MEDLINE | ID: mdl-36196045

Subject(s)
Key words

breast cancer; deep learning; digital breast tomosynthesis (DBT); generative adversarial networks (GAN); synthetic mammogram

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms / Mammography Type of study: Prognostic_studies Limits: Female / Humans Language: En Journal: Med Phys Year: 2023 Document type: Article Country of publication: Estados Unidos

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