Multimodal Representation Learning for Blastocyst Assessment-Reference-Cited by-同舟云学术

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Multimodal Representation Learning for Blastocyst Assessment

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Wang Youcheng¹,Zheng Zhe¹,Ni Na¹,Tong Guoqing²,Cheng Nuo³,Li Kai³,Yin Ping³,Chen Yuanyuan³,Wu Yingna¹,Xie Guangping¹

Affiliation:

1. ShanghaiTech University,Center for Adaptive System Engineering, School of Creativity and Art,Shanghai,China

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

3. Shuguang Hospital,Reproductive Medicine Center,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230468.pdf?arnumber=10230468

Reference22 articles.

1. The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting

2. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

3. Automatic grading of human blastocysts from time-lapse imaging

4. Predicting Human Embryos' Implantation Outcome from a Single Blastocyst Image

5. Using Deep Learning with Large Dataset of Microscope Images to Develop an Automated Embryo Grading System

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multimodal Local Representation Learning for Multi-Task Blastocyst Assessment;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

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