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在卷积神经网络的训练过程中,将标签不确定性纳入训练过程中,可以提高多巴胺载体SPECT中某些和不确定的病例之间的歧视性能

Aleksej Kucerenko¹, Thomas Buddenkotte², Ivayla Apostolova²

¹xAILab Bamberg, Chair of Explainable Machine Learning, Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-University, Bamberg, Germany.

European journal of nuclear medicine and molecular imaging

|November 26, 2024

概括

将读者不确定性纳入深层卷积神经网络 (CNN) 对多巴胺载体 (DAT) -SPECT解释的训练,可以改善对某些不确定的病例的歧视,而不会影响整体准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经科学是一个神经科学.

背景情况:

深层卷积神经网络 (CNN) 在解释多巴胺载体 (DAT) -SPECT扫描中显示出潜力.
准确的解释需要区分某些与不确定的情况,以防止错误分类.
有效地传达不确定性对于临床实用性至关重要.

研究的目的:

评估将标签不确定性纳入CNN培训的方法.
为了增强CNN sigmoid输出的实用性,以区分某些不确定的DAT-SPECT案例.
为了提高CNN辅助DAT-SPECT解释的可靠性.

主要方法:

三个数据集的回顾性分析 (开发,两个独立的分布外数据集).
使用随机投票训练 (RVT),平均投票训练 (AVT) 或多数投票训练 (MVT) 进行训练的ResNet-18架构.
对"不确定的"和"确定的"西格体输出进行评估的平衡准确性.

主要成果:

与MVT相比,RVT和AVT方法减少了在"某些"案例中需要高平衡精度的"不确"案例的比例.
在所有测试案例中,RVT和AVT的整体平衡精度略高.
这些改进在所有测试数据集中都是一致的.

结论:

在CNN培训中将读者之间的差异纳入CNN培训中,增强了某些不确定的DAT-SPECT案件的歧视.
这种方法可以提高CNN输出的可靠性,而不会对整体分类准确性产生负面影响.
这些发现表明,DAT-SPECT扫描的CNN辅助解释更为强大.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.多巴胺运输体是多巴胺的运输体.在FP-CIT中.斯佩克特 (Spectre) 是一个运动场.不确定性不确定性

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