Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data.

Yin, Shi; Peng, Qinmu; Li, Hongming; Zhang, Zhengqiang; You, Xinge; Fischer, Katherine; Furth, Susan L; Tasian, Gregory E; Fan, Yong

Yin, Shi; Peng, Qinmu; Li, Hongming; Zhang, Zhengqiang; You, Xinge; Fischer, Katherine; Furth, Susan L; Tasian, Gregory E; Fan, Yong.

Afiliação

Yin S; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
Peng Q; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Li H; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
Zhang Z; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
You X; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
Fischer K; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
Furth SL; Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Tasian GE; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Fan Y; Department of Pediatrics, Division of Pediatric Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.

Proc IEEE Int Symp Biomed Imaging ; 2020: 1347-1350, 2020 Apr.

Article em En | MEDLINE | ID: mdl-33850604

Palavras-chave

Multi-instance deep learning; congenital abnormalities of the kidney and urinary tract; sagittal view; transverse view; ultrasound images

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Proc IEEE Int Symp Biomed Imaging Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

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