从解释到干预:从放射学中使用的卷积神经网络进行交互式知识提取
Kwun Ho Ngan1,2, Esma Mansouri-Benssassi2, James Phelan1
1Data Science Institute, City, University of London, London, United Kingdom.
PloS one
|April 10, 2024
概括
本研究引入了一个交互式框架,以增强临床医生对卷积神经网络 (CNN) 的信任,用于医学X射线分析. 它通过将CNN层转化为象征性规则,使人工智能决策可解释,从而允许专家干预.
科学领域:
- 医疗成像中的人工智能
- 机器学习的可解释性
- 临床决策支持系统 临床决策支持系统
背景情况:
- 深度学习模型,特别是卷积神经网络 (CNN),擅长从医学X射线图像中提取复杂的特征.
- 由于缺乏临床医生的信任,CNN的有限解释性阻碍了它们在临床实践中的采用.
- 弥合AI能力和临床接受之间的差距需要可解释的AI解决方案.
研究的目的:
- 开发一个交互式框架,以增加临床医生对基于CNN的医学图像分析的信任.
- 让临床医生能够质疑和干预CNN的决策过程.
- 创建数据驱动的CNN的可解释的精细化,与最佳医疗实践保持一致.
主要方法:
- 将一层受过训练的CNN翻译成一套紧的象征性规则.
- 利用专家互动与规则可视化来促进临床相关的CNN内核使用.
- 采用放射学分析和换评估来定义和验证内核相关性.
- 在不影响模型性能的情况下删除临床上无意义的CNN内核.
主要成果:
- 该框架成功地将CNN层转化为可解释的符号规则.
- 临床医生与规则可视化的互动确定并促进了临床相关的CNN内核.
- 删除无意义的内核并没有对整体模型性能产生负面影响.
- 这种方法产生了一个可解释的CNN精细化,与临床专业知识保持一致.
结论:
- 拟议的互动框架通过使CNN决策透明和可控,提高了临床医生的信任.
- 符号规则翻译和专家指导的内核改进是放射学中可解释AI的有效策略.
- 这种方法通过确保与医学最佳实践保持一致,促进了人工智能工具在临床工作流程中的整合.
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