从动态咳音频数据中维持COVID-19检测模型性能的一个全面的漂移适应框架:模型开发和验证.
Theofanis Ganitidis1, Maria Athanasiou1, Konstantinos Mitsis1
1School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
这项研究开发了一个框架,以打击COVID-19音频检测中的模型漂移. 积极学习显著提高了模型的准确性,确保了可靠的诊断工具.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 由于COVID-19的流行,需要适应性的诊断工具.
- 机器学习,特别是卷积神经网络,显示出疾病检测的前景.
- 模型漂移,由改变数据分布引起,随着时间的推移降低了性能.
研究的目的:
- 创建一个监测和减轻COVID-19音频检测模型漂移的框架.
- 将机器学习模型适应不断变化的数据特征.
主要方法:
- 使用了两个众包数据集 (COVID-19声音,Coswara).
- 使用最大平均差异来检测数据分布变化和模型漂移.
- 研究了无监督的领域适应和模型再培训的积极学习.
主要成果:
- 基线模型显示,在开发后的时期,性能下降,表明漂移.
- 无监督域调整提高了高达24%的平衡精度.
- 积极学习提高了高达60%的平衡精度.
结论:
- 拟议的框架有效地解决了COVID-19检测中的模型漂移问题.
- 持续的适应确保了持续的模型性能,以实现可靠的诊断.
- 这种方法可以应用于其他传染病检测工具.
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