Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

Oh, Jang-Hoon; Kim, Hyug-Gi; Lee, Kyung Mi; Ryu, Chang-Woo

Oh, Jang-Hoon; Kim, Hyug-Gi; Lee, Kyung Mi; Ryu, Chang-Woo.

Affiliation

Oh JH; Department of Biomedical Science and Technology, Graduate School, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea.
Kim HG; Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea.
Lee KM; Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea. Electronic address: bandilee@khu.ac.kr.
Ryu CW; Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, #892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea. Electronic address: md.cwryu@gmail.com.

Eur J Radiol ; 154: 110369, 2022 Sep.

Article de En | MEDLINE | ID: mdl-35691109

Sujet(s)
Mots clés

Artificial intelligence; Deep learning; Explainability; Interpretability; Mammography; Phantom; Quality Control

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Tumeurs du sein / Apprentissage profond Type d'étude: Prognostic_studies Limites: Female / Humans Langue: En Journal: Eur J Radiol Année: 2022 Type de document: Article Pays de publication: Irlande

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