自我监督学习在胚胎选择中的潜力,促进试管婴儿的成功

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guanqiao Shan, Yu Sun
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引用次数: 0

摘要

如何选择 "最佳 "胚胎进行移植是临床体外受精(IVF)中一个长期存在的问题。Wang 等人提出了一种用于人类胚胎选择的多模态自监督学习框架,具有较高的准确性和泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The potential of self- supervised learning in embryo selection for IVF success

How to select the “best” embryo for transfer is a long-standing question in clinical in vitro fertilization (IVF). Wang et al. proposed a multi-modal self-supervised learning framework for human embryo selection with a high accuracy and generalization ability.

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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
期刊介绍:
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