不确定性估计和分布外检测用于基于深度学习的图像重建使用本地Lipschitzz
IEEE journal of biomedical and health informatics
|May 24, 2024
概括
这项研究引入了一种新的方法,使用本地Lipschitz度量来准确识别分布外的医疗图像,显著提高深度学习重建任务的诊断准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 准确的医学图像重建对于诊断至关重要.
- 监督深度学习模型与未见的数据分布作斗争.
- 现有的不确定性估计方法不评估培训分布的适应性.
研究的目的:
- 在深度学习医学成像中,开发一种用于区分分布中的图像和分布外的图像的方法.
- 在遇到新数据时评估深度学习模型的可靠性.
- 为了提高重建医疗图像的诊断准确度.
主要方法:
- 提出一种基于当地的利普希茨度量测量方法来检测分布外的图像.
- 验证了使用AUTOMAP架构进行磁共振成像 (MRI) 重建的方法.
- 使用UNET架构对MRI无效化和计算机断层扫描 (CT) 重建的扩展验证.
主要成果:
- 实现了99.94%的曲线下的面积 (AUC) 来区分分布中的图像和分布外的图像.
- 在局部利普希茨值和平均绝对误差 (MAE) 之间证明了强烈的相关性 (斯皮尔曼的rho = 0.8475).
- 超越了基线方法,如蒙特卡洛脱落,深层合奏和平均方差估计.
结论:
- 当地的Lipschitz指标有效地识别了医疗成像中的分布外数据.
- 局部利普希茨值和MAE之间的相关性可以指导数据增量和不确定性减少.
- 提出的方法是多功能,适用于各种架构和医学成像任务,提高诊断可靠性.
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