通过使用胸部X射线图与像素级标签来更好地检测深度学习肺结节的性能,与计算机断层扫描相关:数据质量很重要
Jae Yong Kim1, Wi-Sun Ryu2, Dongmin Kim1
1Artificial Intelligence Research Center, JLK Inc., 5 Teheran-ro 33-gil, Seoul, Republic of Korea.
Scientific reports
|July 10, 2024
概括
高质量的,像素级注释数据参考胸部CT扫描显著改善深度学习模型的肺结节检测在胸部X射线图. 与图像级标签相比,这种方法可以提高节点定位的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 在深度学习模型中进行胸部放射学查的标记错误可能会导致显著的性能下降.
- 肺结节的检测特别容易出现错误,因为正常放射和那些由肋骨遮蔽结节的放射相似.
研究的目的:
- 调查数据标签质量和模式对肺结节检测深度学习模型性能的影响.
- 为了比较像素级与图像级注释以及胸部X射线与胸部计算机断层扫描 (CT) 数据的有效性.
主要方法:
- 使用DenseNet架构与挤压和激发块训练了四个深度学习模型.
- 利用国家卫生研究院胸部放射和AI-HUB基于CT的数据集,包括图像级和像素级的注释.
- 在两个外部验证数据集上评估模型性能.
主要成果:
- 基于CT的标签 (AI-HUB数据集) 与图像级注释的标签表现优于基于胸部X-ray的标签 (NIH数据集) (AUC 0.88对0.71和0.78对0.73).
- 像素级注释CT数据产生了最佳的性能 (AUC0.91和0.86) 和优越的结节定位 (子0.360.58).
结论:
- 准确的,像素级注释数据,特别是参考CT扫描时,对于开发可靠的深度学习算法来检测肺结节至关重要.
- 通过像素级注释来提高数据质量,可以显著提高胸部X射线分析中的检测和定位精度.
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