Open AccessEnd-to-end deep learning for recognition of ploidy status using time-lapse videos
Author(s) -
Chun I. Lee,
Yan Su,
Chien Hong Chen,
T. Arthur Chang,
Esther En Shu Kuo,
Wandong Zheng,
Wen-Shyang Hsieh,
Chun Chia Huang,
Maw Sheng Lee,
Mark Liu
Publication year - 2021
Publication title -
journal of assisted reproduction and genetics
Language(s) - English
Resource type - Journals
eISSN - 1573-7330
pISSN - 1058-0468
DOI - 10.1007/s10815-021-02228-8
Subject(s) - aneuploidy , deep learning , artificial intelligence , receiver operating characteristic , computer science , machine learning , biology , genetics , gene , chromosome
Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video.