为了稳固的心电图分类,设计相对网络架构
Christopher Wiedeman1, Ge Wang2
1Department of Electrical and Computer Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Patterns (New York, N.Y.)
|January 8, 2025
概括
这项研究引入了一种新的组合方法,使用特征去关系和富里埃分区来增强人工智能 (AI) 对医疗任务的信任. 这种方法改善了AI.
科学领域:
- 人工智能在医学中的应用
- 机器学习安全 机器学习安全
- 信号处理 信号处理
背景情况:
- 值得信赖的AI对于患者关键的医疗应用至关重要.
- 目前用于不确定性估计的集合方法容易受到对抗性攻击.
- 整体模型之间的共同漏洞限制了它们的稳定性.
研究的目的:
- 为AI在医疗任务中开发一种强大的整体方法.
- 增强AI识别不确定性和抵制对抗攻击的能力.
- 提高AI在患者关键应用中的可靠性.
主要方法:
- 提出了一种组合方法,将特征对比和富里埃分区结合起来.
- 训练有素的网络学习各种特征,减少对干扰的敏感性.
- 对心电图 (ECG) 数据的白盒对抗性攻击进行了测试.
- 适应对抗训练和DVERGE用于组合比较.
主要成果:
- 折叠关系和富里埃分区组合在未被干扰的数据上保持了性能.
- 针对预计的梯度下降和平稳的对抗性攻击,证明了优越的不确定性估计.
- 该方法显示了对不同规模的对抗性攻击的稳定性.
- 在培训期间不需要昂贵的对抗样本优化.
结论:
- 拟议的组合方法增强了AI在医疗任务中的稳定性和不确定性估计.
- 特征脱关系和富里埃分区有效地减少了对敌对攻击的脆弱性.
- 这种方法提供了一种更可靠,更有效的方法,可以为医疗保健建立值得信赖的AI.
- 这些方法适用于其他需要强大的AI模型的领域.
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