在心电图诊断中转移学习:它是否有效?
Cuong V Nguyen1, Cuong D Do1,2
1College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam.
PloS one
|May 19, 2025
概括
对心电图 (ECG) 诊断的转移学习对于小数据集是有用的,但并不总是提高性能. 从头开始的培训可以与较大的数据集进行比较,提供更快的融合和更低的成本.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于有限的标记数据,用于ECG诊断的深度学习面临挑战.
- 转移学习是常用的,但它的优越性比从头开始的培训还没有被系统地证明.
研究的目的:
- 实证地评估转移学习与从头开始培训的有效性,用于多标签心电图分类.
- 调查数据集大小和网络架构如何影响转移学习性能.
主要方法:
- 微调性能与从头开始的训练在各种ECG数据集和深度神经网络架构中进行了比较.
- 分析了数据集大小对性能和趋同的影响.
主要成果:
- 微调是小数据集的首选,但不能保证更好的性能.
- 转移学习的好处随着更大的数据集而减少;从头开始的培训可以通过更多的培训时间获得相似的结果.
- 微调加速了融合,减少了培训时间和计算成本.
- 转移学习与卷积神经网络 (CNN) 相比,与循环神经网络 (RNN) 的兼容性更好.
结论:
- 转移学习在心电图诊断中是有价值的,特别是对于较小的数据集,但它的必要性取决于数据可用性和计算资源.
- 研究人员应该考虑转移学习效益和预培训成本之间的权衡.
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