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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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在多发性硬化症病变细分中的实例级定量突出性.

Federico Spagnolo1,2,3,4, Nataliia Molchanova4,5,6, Meritxell Bach Cuadra5,6

  • 1Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.

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概括
此摘要是机器生成的。

新的可解释人工智能 (XAI) 方法为语义细分提供实例级突出性地图,这对于理解多发性硬化症MRI扫描中白质病变检测至关重要.

关键词:
深度学习是一种深度学习.这就是为什么MRI是MRI.多发性硬化症是多发性硬化症.分段化 分段化 分段化 分段化在XAI,XAI就是XAI.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 可解释的人工智能 (XAI) 方法正在开发用于分类和细分任务.
  • 现有的XAI方法缺乏对语义细分的实例级解释,特别是在特定病变的医学成像中.
  • 了解单个病变的模型决策对于多病变性疾病至关重要.

研究的目的:

  • 开发和验证用于语义细分的实例级解释图.
  • 将这些方法应用于使用MRI数据对多发性硬化症 (MS) 白质病变 (WML) 的细分.
  • 量化评估模型对不同MRI序列的依赖,并识别潜在的错误.

主要方法:

  • 扩展了SmoothGrad和Grad-CAM++以创建具有定量突出性的实例级解释图.
  • 在687名多发性硬化患者的4023张MRI扫描上使用3D U-Net,nnU-Net和Swin UNETR应用方法对WML细分.
  • 计算了突出度地图并分析了它们在不同预测类型 (TP,FN,FP,TN) 的分布.

主要成果:

  • 实例突出性地图表明,模型在WML细分方面优先考虑FLAIR而不是MPRAGE.
  • 度地图表明,FLAIR高强度和周围健康的白质是WML检测的关键.
  • 对突出性地图峰值的定量分析显示了真正,假正,假负和真负预测之间的显著差异,这表明了错误识别能力.

结论:

  • 引入了新的XAI方法,用于语义细分中的定量,实例级解释.
  • 拟议的XAI地图是无关架构的,可以提高模型性能,优化架构并增强用户的信任.
  • 这些方法为医疗图像分析中的AI决策提供了病变特定的理由.