在分布外检测与分布内投票,使用医学例子的胸部X射线分类
Alessandro Wollek1, Theresa Willem2, Michael Ingrisch3
1Munich Institute of Biomedical Engineering and the School of Computation, Information, and Technology, Technical University of Munich, Munich, Germany.
Medical physics
|October 13, 2023
概括
深度学习模型在与现实世界外分发 (OOD) 数据进行斗争. 拟议的分布式投票 (IDV) 方法显著提高了胸部X射线分类的稳定性,与OOD放射图相比.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习模型表现出成功,但面临着与真实世界数据的挑战.
- 在特定数据集上训练的模型在遇到意想不到的OOD数据时表现不佳.
研究的目的:
- 评估OOD放射图对胸部X射线分类模型的影响.
- 在医学成像中增强对OOD数据的模型稳定性.
主要方法:
- 在胸部X射线14,IRMA,骨年龄,MURA和ImageNet数据集上测试了CheXnet模型.
- 在多标签分类中用于OOD检测的拟议和评估的分布内投票 (IDV).
- 使用AUC分析将IDV与基于Mahalanobis的检测,MaxLogit,MaxEnergy,SS OOD和CutMix进行了比较.
主要成果:
- 未经训练的模型未能丢弃OOD图像 (AUC0.5).
- IDV的平均OOD AUC为0.999,优于其他方法.
- 仅在ImageNet数据上训练的IDV显示AUC为0.913,明显优于替代品.
结论:
- 大多数OOD检测方法在放射数据上表现不佳,除了Mahalanobis和IDV.
- 仅仅在ID数据上的培训就增加了OOD图像的错误阳性.
- IDV提高了分类性能,而不增加推断开销或降低目标分类准确度.
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