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Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring.
Liao, Qiuyue; Zhang, Qi; Feng, Xue; Huang, Haibo; Xu, Haohao; Tian, Baoyuan; Liu, Jihao; Yu, Qihui; Guo, Na; Liu, Qun; Huang, Bo; Ma, Ding; Ai, Jihui; Xu, Shugong; Li, Kezhen.
  • Liao Q; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Zhang Q; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Feng X; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Huang H; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Xu H; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Tian B; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Liu J; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Yu Q; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
  • Guo N; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Liu Q; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Huang B; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Ma D; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Ai J; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. jihuiai@tjh.tjmu.edu.cn.
  • Xu S; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China. shugong@shu.edu.cn.
  • Li K; Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. tjkeke@126.com.
Commun Biol ; 4(1): 415, 2021 03 26.
Article en En | MEDLINE | ID: mdl-33772211
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Blastocisto / Algoritmos / Desarrollo Embrionario / Imagen de Lapso de Tiempo / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article
Texto completo
Imprimir
XML
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Blastocisto / Algoritmos / Desarrollo Embrionario / Imagen de Lapso de Tiempo / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article