End-to-end deep learning for recognition of ploidy status using time-lapse videos.

Journal: Journal of assisted reproduction and genetics
Published Date: Jul 1, 2021

Abstract

PURPOSE: Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video.

Authors

  • Chun-I Lee
    Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.
  • Yan-Ru Su
    Binflux Inc., Taipei, Taiwan.
  • Chien-Hong Chen
    Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
  • T Arthur Chang
    Department of Obstetrics and Gynecology, University of Texas Health Science Center, San Antonio, TX, USA.
  • Esther En-Shu Kuo
    Binflux Inc., Taipei, Taiwan.
  • Wei-Lin Zheng
    Binflux Inc., Taipei, Taiwan.
  • Wen-Ting Hsieh
    Binflux Inc., Taipei, Taiwan.
  • Chun-Chia Huang
    Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
  • Maw-Sheng Lee
    Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.
  • Mark Liu
    Tisch Cancer Institute, Mount Sinai Hospital, New York, NY, 10029, USA.

Keywords

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