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不确定性意识深度学习 膝盖放射图的特征化用于大规模注册表创建
Kellen L Mulford1, Austin F Grove1, Elizabeth S Kaji1
1Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota.
The Journal of arthroplasty
|October 30, 2024
概括
一个自动化管道使用多标签分类器和物体检测准确地描述膝盖放射图. 符合性预测通过量化图像分析中的不确定性来提高模型透明度.
科学领域:
- 医学成像分析分析 医学成像分析
- 机器学习在放射学中的应用.
- 骨科成像信息学 骨科成像信息学
背景情况:
- 开发一个自动化图像摄入管道,用于膝盖放射学注册表,对于高效的数据管理和分析至关重要.
- 集成先进的机器学习模型,包括多标签图像语义分类器和对象检测,可以显著提高膝盖放射图的特征.
- 使用符合性预测量化不确定性为模型的可靠性提供了必要的透明度,特别是在关键的临床应用中.
研究的目的:
- 开发和验证一个自动化图像摄入管道,用于膝盖放射.
- 实施多标签图像语义分类器,用于膝盖放射分析的不确定性量化.
- 整合一个物体检测模型,用于在X射线图中识别膝盖硬件.
主要方法:
- 标注了26,000张膝盖图像,用于存在,横向,假肢和视图;在11,841张图像中标注了手术构造位置.
- 训练了一种基于EfficientNet的多标签的不确定性意识分类器,用于识别膝盖横向性,植入物和视图,以及一个域检测分类器.
- 开发了20种不同的膝盖植入物的物体检测模型,使用F1得分,准确性,灵敏度和内部和外部数据集的特异性来评估性能.
主要成果:
- 该分类模型使用符合性预测实现了F1分数>0.98,覆盖率>0.99,效率为0.97.
- 域检测模型显示F1得分为0.99,膝盖X射线图的精度和回忆为0.99.
- 对象检测模型在所有植入物类中实现了0.945的平均平均精度,平均精度和回忆分别为0.950和0.886.
结论:
- 一个集成多标签分类器,域检测和对象检测的自动化管道有效地描述了膝盖放射.
- 合规预测通过为分类模型提供可靠的不确定性估计,大大提高了透明度.
- 这种方法提供了一个强大的解决方案,用于自动化分析和数据管理,膝盖X光学注册表.
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