适应性元数据引导的监督对比学习对呼吸声分类的域适应
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
这项研究通过使用元数据来增强呼吸声分类 (RSC) 模型,以减少记录不一致性和人口不平衡的偏差. 新方法显著提高了模型的准确性,并减少了对现实应用的域依赖.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 呼吸声分类 (RSC) 的深度学习模型面临挑战,原因是来自不一致的记录和不平衡的人口统计数据的偏见数据集.
- 这种偏见导致模型在现实世界的场景中部署时表现不佳.
研究的目的:
- 通过探索元数据引导的域调整技术,解决RSC数据集中的偏见.
- 提高RSC模型在不同数据源中的通用性和准确性.
主要方法:
- 研究了各种元数据属性及其组合对RSC模型性能的影响.
- 开发了一种先进的方法,用于对元数据组合进行适应性调整,以提高训练期间的域适应性.
主要成果:
- 对RSC模型的域依赖性显著降低.
- 实现了84.97%的检测准确度得分,比基线模型提高了7.37%.
- 在ICBHI数据集和定制数据集上验证了拟议方法的有效性.
结论:
- 以元数据为指导的域调整在缓解偏差和改善RSC模型性能方面是有效的.
- 拟议的适应性重新调度方法为训练强大而准确的呼吸声分类模型提供了实质性的进步.
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