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Correction: Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy.
Fang, Teng; Yuan, Pengbo; Gong, Chen; Jiang, Yueping; Yu, Yuezhou; Shang, Wenhao; Tian, Chan; Ye, Anpei.
Affiliation
  • Fang T; Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China. yap@pku.edu.cn.
  • Yuan P; National Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China. tianchan@bjmu.edu.cn.
  • Gong C; National Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China. tianchan@bjmu.edu.cn.
  • Jiang Y; Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Center for Reproductive Medicine, Peking University Third Hospital, Beijing 100191, China.
  • Yu Y; National Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China. tianchan@bjmu.edu.cn.
  • Shang W; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
  • Tian C; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
  • Ye A; National Clinical Research Center for Obstetrics and Gynecology, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China. tianchan@bjmu.edu.cn.
Analyst ; 147(10): 2280, 2022 May 17.
Article in En | MEDLINE | ID: mdl-35481470
ABSTRACT
Correction for 'Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy' by Teng Fang et al., Analyst, 2022, https//doi.org/10.1039/D2AN00024E.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Analyst Year: 2022 Document type: Article Affiliation country: China
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Analyst Year: 2022 Document type: Article Affiliation country: China