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自主监督机器学习的强大和数据效率高的概括,用于诊断成像

Shekoofeh Azizi¹, Laura Culp², Jan Freyberg²

¹Google Research, Mountain View, CA, USA. shekazizi@google.com.

Nature biomedical engineering

|June 8, 2023

概括

一个名为REMEDIS的新策略增强了用于医学成像的机器学习. 它通过使用自我监督和转移学习来提高模型的准确性和效率,特别是在新的环境中.

科学领域:

人工智能的人工智能
医疗成像医学成像
机器学习机器学习

背景情况:

医学中的机器学习模型可以与人类专家相提并论.
模型的性能往往在被应用于其训练分布之外的数据时显著降低.
这限制了人工智能在医疗保健中的实际应用.

研究的目的:

为医疗成像任务引入一种新的表示-学习策略,REMEDIS.
提高模型的稳定性和培训效率,特别是在分布之外的场景中.
在遇到新数据时减轻机器学习模型的性能退化.

主要方法:

雷梅迪斯将大规模的监督转移学习与自然图像相结合.
它结合了医学图像的中间对比自主监督学习.
该策略需要最小的特定任务定制.

主要成果:

与监督基线相比,REMEDIS提高了在分发中的诊断准确率高达11.5%.
在非分销环境中,REMEDIS只需要1-33%的数据进行再培训,以匹配基线表现.
该策略在6个成像领域和15个测试数据集中展示了实用性.

结论:

在医学成像中,REMEDIS提供了一种强大而高效的机器学习方法.
该战略有效地应对了分销之外性能恶化的挑战.
雷梅迪斯有可能加速人工智能工具在医学诊断中的开发和部署.

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