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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
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提高胸部X射线解释模型的公平性,使用注意力驱动的蒙面图像建模.

Chao-Ju Chen, Stephanie Wang, Po-Chih Kuo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    这项研究揭示了人工智能 (AI) 在医学成像方面的偏见. 开发了一种以注意力驱动的蒙面图像建模 (AMIM) 方法,以减少胸部X射线分析中的人口差异.

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 健康 公平 卫生 公平

    背景情况:

    • 人们越来越担心医疗保健中使用的人工智能 (AI) 模型中的人口偏见.
    • 人工智能模型可能会延续甚至放大现有的健康差异.
    • 确保在多样化的患者群体中实现公平的AI性能对于医疗应用至关重要.

    研究的目的:

    • 调查视觉变压器 (ViT) 模型中的人口偏差,用于胸部X射线 (CXR) 分析.
    • 开发和评估一种新的方法,即以注意力驱动的蒙面图像建模 (AMIM),以减轻已识别的偏见.
    • 提高AI在医疗诊断工具中的公平性和公平性.

    主要方法:

    • 使用视觉变压器 (ViT) 模型对CXRs进行多标签分类.
    • 员工转移学习以评估模型识别种族人口结构的能力.
    • 分析了注意力热图模型,以确定可能编码人口信息的区域.
    • 开发并应用了以注意力驱动的掩面图像建模 (AMIM) 带有掩面补丁来减少偏差.

    主要成果:

    • 对于CXR分类,ViT模型实现了接收器运行特征曲线 (AUC) 下的面积为0.72.
    • 该模型在识别种族人口统计学方面显示AUC为0.66,尽管在培训中没有人口统计数据.

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  • 注意热图分析突出显示了可能与人口信息相关的图像区域.
  • 使用9个掩盖贴片的AMIM方法导致AUC为0.71,表明人口差异减少.

    • 结论:

    • 视觉变压器模型可以无意中从医疗图像中学习人口统计信息.
    • 以注意力驱动的蒙面图像建模 (AMIM) 在减轻人工智能驱动的医学成像中的人口偏见方面显示出希望.
    • 这项研究有助于为医疗保健应用开发更公平,更可靠的AI工具.