Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder.

Jung, Yuyeon; Kim, Taewan; Han, Mi-Ryung; Kim, Sejin; Kim, Geunyoung; Lee, Seungchul; Choi, Youn Jin

Jung, Yuyeon; Kim, Taewan; Han, Mi-Ryung; Kim, Sejin; Kim, Geunyoung; Lee, Seungchul; Choi, Youn Jin.

Afiliação

Jung Y; Department of Obstetrics and Gynecology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea.
Kim T; Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
Han MR; Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea.
Kim S; Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Kim G; Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Lee S; Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea. seunglee@postech.ac.kr.
Choi YJ; Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea. seunglee@postech.ac.kr.

Sci Rep ; 12(1): 17024, 2022 10 11.

Article em En | MEDLINE | ID: mdl-36220853

Assuntos

Redes Neurais de Computação; Neoplasias Ovarianas; Feminino; Humanos; Neoplasias Ovarianas/diagnóstico por imagem; Curva ROC

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Redes Neurais de Computação Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Female / Humans Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article

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