Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments.

Saida, Tsukasa; Mori, Kensaku; Hoshiai, Sodai; Sakai, Masafumi; Urushibara, Aiko; Ishiguro, Toshitaka; Minami, Manabu; Satoh, Toyomi; Nakajima, Takahito

Saida, Tsukasa; Mori, Kensaku; Hoshiai, Sodai; Sakai, Masafumi; Urushibara, Aiko; Ishiguro, Toshitaka; Minami, Manabu; Satoh, Toyomi; Nakajima, Takahito.

Afiliação

Saida T; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Mori K; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Hoshiai S; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Sakai M; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Urushibara A; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Ishiguro T; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Minami M; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Satoh T; Department of Obstetrics and Gynecology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.
Nakajima T; Department of Radiology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.

Cancers (Basel) ; 14(4)2022 Feb 16.

Article em En | MEDLINE | ID: mdl-35205735

Palavras-chave

artificial intelligence; carcinoma; convolutional neural network; magnetic resonance imaging; ovary

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies Idioma: En Revista: Cancers (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão País de publicação: Suíça

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