机器学习算法的概括能力,用于检测微电极记录中的亚体体核
Thibault Martin1, Pierre Jannin1, John S H Baxter2
1Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.
概括
在深度大脑刺激 (DBS) 中使用微电极记录 (MER) 的机器学习模型显示,在不同临床中心的性能下降. 转移学习有效地提高了通用性,使这些人工智能工具对神经外科医生更可靠.
科学领域:
- 神经外科 神经外科
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 微电极记录 (MER) 对于指导深度大脑刺激 (DBS) 电极放置至关重要.
- 机器学习 (ML) 模型越来越多地用于帮助神经生理学家解释MERs.
- 确保ML模型在不同临床环境中的通用性对于其实际应用至关重要.
研究的目的:
- 评估MER分类的ML算法在不同临床中心和培训范式中的通用性.
- 评估领域转移对应用到MER数据的深度学习模型性能的影响.
- 研究方法来提高基于ML的MER分析的稳定性和适用性.
主要方法:
- 实施了五种深度学习算法,用于MER信号的二进制分类.
- 利用来自两个不同的临床中心的三个MER数据库,尺寸,硬件和注释各不相同.
- 经过训练和测试的算法使用直接传输,微调和从头开始的训练来评估可通用性和数据库效应.
主要成果:
- 在销售之外的测试显示,ML模型的性能显著下降 (6.5%-16.0%的平衡精度下降).
- 使用转移学习的微调有效地改善了性能退化.
- 从零开始重新训练显示出与微调相似的性能,但需要更长的训练时间.
结论:
- 可通用性是临床采用神经外科ML算法的关键因素.
- 用于MER分类的ML算法容易受到域移动的影响,但可以使用转移学习快速适应.
- 简单的转移学习程序可以在新的临床环境中提高基于ML的MER分析的可靠性.
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