Deep Learning-Based Quantitative Blastocyst Assessment-Reference-Cited by-同舟云学术

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Deep Learning-Based Quantitative Blastocyst Assessment

Published:2023-07-24 Issue: Volume: Page:
ISSN:
Container-title:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Zheng Zhe¹,Wang Youcheng¹,Ni Na¹,Tong Guoqing²,Cheng Nuo³,Yin Ping³,Chen Yuanyuan³,Wu Yingna¹,Xie Guangping¹,Yang Tingting¹

Affiliation:

1. ShanghaiTech University,Center for Adaptive System Engineering,Shanghai,China

2. The First Affiliated Hospital of Xi’an Jiaotong University,Department of Reproductive Medicine,Xi’an,China

3. Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Reproductive Medicine Center,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339936/10339939/10340963.pdf?arnumber=10340963

Reference12 articles.

1. Psychological well-being of infertile women and its relationship with demographic factors and fertility history: a cross-sectional study

2. Cleavage stage versus blastocyst stage embryo transfer in assisted conception

3. Live birth rate is significantly higher after blastocyst transfer than after cleavage-stage embryo transfer when at least four embryos are available on day 3 of embryo culture. A randomized prospective study

4. In vitro culture of human blastocysts. Toward reproductive certainty: Fertility and genetics beyond. Jansen, R., Mortimer, D;Gardner,1999

5. Vocal cord lesions classification based on deep convolutional neural network and transfer learning

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