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Updated: Jun 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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医学成像中的突出性驱动可解释的深度学习:将视觉可解释性和统计定量分析相结合.

Yusuf Brima1, Marcellin Atemkeng2

  • 1Computer Vision, Institute of Cognitive Science, Osnabrück University, Osnabrueck, D-49090, Lower Saxony, Germany. ybrima@uos.de.

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概括
此摘要是机器生成的。

这项研究引入了一个新的框架来解释医学成像中的深度学习. 它结合了视觉和统计方法,以提高对AI在医疗保健中的信任和采用.

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科学领域:

  • 医学图像分析 医学图像分析
  • 医疗保健中的人工智能
  • 可解释的人工智能 (XAI)

背景情况:

  • 深度学习模型在医学成像中表现有前途,但缺乏可解释性,阻碍了临床信任和采用.
  • 当前的研究往往依赖于视觉检查,而不是在医学成像中进行定量分析.
  • 可解释性技术对于提高利益相关者对人工智能驱动的医疗保健解决方案的信心至关重要.

研究的目的:

  • 提出和评估基于图像的突出性框架,以提高医学图像分析中的深度学习模型的可解释性.
  • 整合定性和统计定量评估,以全面评估突出性方法.
  • 提高临床环境中深度学习模型的透明度和可信度.

主要方法:

  • 使用基于自适应路径的梯度集成,无梯度技术和类激活映射 (CAM) 衍生品.
  • 将框架应用于脑瘤MRI和COVID-19胸部X射线数据集,使用深度卷积神经网络.
  • 雇员准确性信息曲线 (AIC) 和软max信息曲线 (SIC) 用于对突出性有效性的定量评估.

主要成果:

  • 视觉检查显示ScoreCAM,XRAI,GradCAM和GradCAM++产生了可临床解释的归因图,突出了生物标志物和模型偏差.
  • 经验评估表明,ScoreCAM和XRAI在保留相关图像区域方面是有效的,由更高的AUC值证明.
  • 软马克斯信息曲线 (SICs) 显示了变化,一些随机突出面罩的表现优于既定方法,强调需要综合评估指标.

结论:

  • 结合定性和定量方法对于全面评估至关重要,并提高深度学习模型的透明度和可信度.
  • 选择适合特定医学成像任务的显着性方法对于有效的模型解释性至关重要.
  • 这项工作提升了模型可解释性,以促进更大的信任和医疗保健中人工智能的临床采用,未来的工作重点是改进指标和扩大模式.