Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI.

Lo, Justin; Lim, Adam; Wagner, Matthias W; Ertl-Wagner, Birgit; Sussman, Dafna

Lo, Justin; Lim, Adam; Wagner, Matthias W; Ertl-Wagner, Birgit; Sussman, Dafna.

Afiliação

Lo J; Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Ryerson University, Toronto, ON, Canada.
Lim A; Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada.
Wagner MW; Department of Electrical, Computer and Biomedical Engineering, Faculty of Engineering and Architectural Sciences, Ryerson University, Toronto, ON, Canada.
Ertl-Wagner B; Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada.
Sussman D; Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada.

Front Artif Intell ; 5: 832485, 2022.

Article em En | MEDLINE | ID: mdl-35372832

Palavras-chave

Convolutional Neural Network (CNN); deep learning; fetal MRI; fetal disease; fetal organ anomaly; image classification

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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PubMed Links

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article