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通过基于补丁扰动的评估管道改善可解释的AI:一个COVID-19X射线图像分析案例研究.

Jimin Sun1, Wenqi Shi2, Felipe O Giuste3

  • 1School of Computer Science and Engineering, Georgia Institute of Technology, Atlanta, 30322, USA.

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概括

本研究引入了一种自动补丁扰动方法,用于评估医学成像中的可解释AI (XAI). 这种方法消除了对人类专家的需求,使得人们能够更好地信任和选择用于临床使用的AI工具.

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科学领域:

  • 人工智能的人工智能
  • 医学成像分析 医学成像分析
  • 临床决策支持系统 临床决策支持系统

背景情况:

  • 可解释的人工智能 (XAI) 方法对于对临床决策支持系统的信任至关重要,但由于缺乏自动化策略和人类监督,其评估受到阻碍.
  • 医疗成像中XAI的现有评估方法通常需要显著的人类干预,额外的注释,并且缺乏通用性.

研究的目的:

  • 提出一种基于补丁扰动的自动化方法,用于评估医学成像分析中XAI方法生成的解释的质量.
  • 在XAI评估过程中消除对人类专家和手册注释的依赖.
  • 为从多个角度评估XAI方法提供一套全面的指标,包括正确性,完整性,一致性和复杂性.

主要方法:

  • 在模型重新训练期间使用静态和动态触发器中毒攻击来开发补丁扰动方法,以自动化XAI评估.
  • 介绍了一个全面的评估指标套件 (正确性,完整性,一致性,复杂性),在模型推断过程中应用.
  • 通过使用流行的XAI方法,对COVID-19X射线分类任务应用拟议的评估策略进行了案例研究.

主要成果:

  • 补丁扰动方法成功地自动评估XAI质量,无需人工干预.
  • 拟议的指标提供了XAI方法的多方面的评估,揭示了它们的优缺点.
  • 该案例研究展示了自动化评估策略在现实世界医学成像场景中的实际应用和有效性.

结论:

  • 拟议的自动化补丁扰动工作流提供了一个可泛化和有效的策略,用于评估医学成像中的XAI方法.
  • 这种方法使开发人员能够识别陷并优化XAI解决方案,同时帮助最终用户选择适合临床实践的XAI工具.
  • 自动化评估对于推动XAI在临床决策支持和转化研究中的采用和可靠性至关重要.