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一致的可解释的图像质量评估,用于医学成像.

Caner Ozer1,2, Arda Guler3, Aysel Turkvatan Cansever3

  • 1Computer Engineering Department, Istanbul Technical University, Istanbul, 34467 Türkiye.

Health information science and systems
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本研究引入了一个可解释的医疗图像质量评估系统,使用NormGrad. 它可靠地检测胸部X射线和心脏MRI中的图像质量问题,提高诊断准确度.

关键词:
一致性 一致性 一致性检测外来物体检测外来物体检测图像质量评估 图像质量评估可以解释性 解释性在LVOT检测检测.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 准确的医学图像质量评估对于防止误诊至关重要.
  • 手动质量评估是耗时的,在大型数据集中容易出现错误.
  • 自动化方法缺乏可解释性和可靠的量化.

研究的目的:

  • 提出一个可解释的医疗图像质量评估自动化系统.
  • 为了验证系统在异物检测 (胸部X射线) 和心脏MRI.
  • 量化拟议方法论的可靠性和可解释性.

主要方法:

  • 使用NormGrad,这是一个比较分类器突出性地图与基线检测器的算法.
  • 引入了平均值差异 (DoM) 度量来评估突出检测器的一致性.
  • 在对象CXR和LVOT心脏MRI数据集上验证了系统.

主要成果:

  • 诺姆格拉德显著优于其他具有高指针游戏分数的突出检测器 (0.863为对象-CXR,0.778为LVOT).
  • 该系统在不同模型中显示出高一致性,DoM得分较低 (对象-CXR为0.001,LVOT为0.005).
  • 公共可用的代码和实验促进了可重复性.

结论:

  • 由NormGrad驱动的系统提高了自动化医疗图像质量评估的可靠性.
  • 该DoM指标提供了一种新的方法来评估突出检测器的一致性.
  • 该系统显示出在医学成像质量控制中临床采用的前景.