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医学成像机器学习的可解释性框架

Alan Q Wang1,2, Batuhan K Karaman1,2, Heejong Kim2

  • 1School of Electrical and Computer Engineering, Cornell University-Cornell Tech, New York City, NY 10044, USA.

IEEE access : practical innovations, open solutions
|October 18, 2024
PubMed
概括

医学成像 (MLMI) 机器学习中的解释性需要正式化. 本研究定义了五个核心元素和一个框架,以指导MLMI模型的设计和应用,以便更好地在现实世界中使用.

关键词:
可以解释性 解释性可以解释性的解释性.机器学习是机器学习.医学成像医学成像

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