Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images.

Miao, Kuo; Zhao, Ning; Lv, Qian; He, Xin; Xu, Mingda; Dong, Xiaoqiu; Li, Dandan; Shao, Xiaohui

Miao, Kuo; Zhao, Ning; Lv, Qian; He, Xin; Xu, Mingda; Dong, Xiaoqiu; Li, Dandan; Shao, Xiaohui.

Afiliação

Miao K; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Zhao N; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Lv Q; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
He X; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Xu M; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Dong X; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Li D; Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China.
Shao X; Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.

J Obstet Gynaecol Res ; 49(12): 2910-2917, 2023 Dec.

Article em En | MEDLINE | ID: mdl-37696522

Assuntos

Doenças dos Anexos; Neoplasias Ovarianas; Humanos; Feminino; Estudos Retrospectivos; Neoplasias Ovarianas/patologia; Ultrassonografia; Doenças dos Anexos/patologia; Ultrassonografia Doppler em Cores; Sensibilidade e Especificidade; Diagnóstico Diferencial

Palavras-chave

ResNet; benign or malignant; deep learning; ovarian tumor; ultrasound

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Doenças dos Anexos Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Revista: J Obstet Gynaecol Res Assunto da revista: GINECOLOGIA / OBSTETRICIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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