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热图解释对于基于深度学习的心电图分析的有用性
Andrea M Storås1,2, Ole Emil Andersen3,4, Sam Lockhart5
1Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, 0167 Oslo, Norway.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
深度神经网络可以从心电图中预测性别,但Grad-CAM解释方法没有为医生提供有用的见解. 需要进一步的研究来解释AI在临床环境中.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医疗保健
- 心脏病学 信息学 信息学
背景情况:
- 深度神经网络 (DNN) 提供了分析复杂医疗数据和帮助诊断的潜力.
- DNN的"黑盒子"性质阻碍了理解和临床信任.
- 可解释的人工智能 (XAI) 方法旨在消除DNN预测的神秘性.
研究的目的:
- 从心电图 (ECG) 开发使用转移学习进行性别预测的DNN.
- 评估Grad-CAM热图在ECG分析中解释DNN预测的临床实用性.
- 评估XAI生成的ECG特征是否对医疗专业人员来说是可解释和有用的.
主要方法:
- 使用转移学习来构建DNN模型.
- 电心电图 (ECG) 数据用于性别预测.
- 使用梯度加权类激活映射 (Grad-CAM) 来生成可视热图,以实现模型的可解释性.
- 医生对Grad-CAM热图的有用性提供了反.
主要成果:
- 一个DNN模型成功地开发出来,可以从心电图中预测性别.
- 格拉德-CAM生成了热图,突出显示了DNN使用的ECG区域.
- 医生发现Grad-CAM热图在临床上没有用处,也没有帮助人们理解性别预测.
- 在热图中发现的心电图特征并未被临床医生认为对性别有歧视性.
结论:
- 目前基于Grad-CAM的XAI方法并不能为基于DNN的ECG的性别预测提供有意义的临床见解.
- 在这种情况下,开发的XAI方法目前不适合临床应用.
- 需要在临床诊断中对DNN进行新的,医学上量身定制的解释技术.
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