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The potential of self- supervised learning in embryo selection for IVF success
Guanqiao Shan1, Yu Sun1,2
1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada.
Patterns (New York, N.Y.)
|July 31, 2024
Abstract:
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.

