随机共教用于训练具有未知的标签噪声水平的神经网络
Bob D de Vos1, Gino E Jansen2,3, Ivana Išgum1,4,5
1Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Scientific reports
|October 6, 2023
概括
这项研究引入了一种新的联合教学方法,以训练神经网络,即使在医学数据集中标签噪声的未知水平. 该方法通过使用随机性来有效地识别和管理噪音标签来增强监督学习.
科学领域:
- 机器学习 机器学习
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 神经网络的监督训练受到标签噪声的严重阻碍.
- 获得高质量的,基于共识的医疗标签是非常昂贵的.
- 标签噪声减轻现有的方法往往需要事先了解噪声水平.
研究的目的:
- 开发一种联合教学方法,有效地处理标签噪声,而无需事先了解其范围.
- 提高神经网络训练在存在未知的标签噪声时的稳定性,特别是用于医疗应用.
主要方法:
- 提出了一种新的联合教学方法,结合随机性来选择或拒绝培训实例.
- 该方法在培训过程中动态识别和减轻噪音标签的影响.
- 对具有不同噪音水平和现实世界医疗任务的合成数据集进行了广泛的评估 (电图分类,心脏MRI细分).
主要成果:
- 拟议的方法在广泛的合成标签噪声水平中显示出稳定性.
- 有效地应用于现实世界的医学分类和细分任务.
- 该方法对超参数选择不敏感,表明其广泛适用性.
结论:
- 开发的联合教学策略成功地应对了监督学习中未知标签噪声的挑战.
- 这种方法为杂的医疗数据集提供了切实可行的解决方案,减少了对昂贵专家共识的需求.
- 该技术在提高AI模型在关键医疗应用中的可靠性方面表现有前途.
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