DC-AAE:双通道对抗自动编码器,具有多任务学习,用于膝关节放射图中的KL级分类
Muhammad Umar Farooq1, Zahid Ullah2, Asifullah Khan3
1Department of IT, Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea.
Computers in biology and medicine
|October 28, 2023
概括
这项研究引入了一种新的半监督深度学习方法,用于使用Kellgren和Lawrence (KL) 分级来分类膝关节关节炎 (OA) 严重程度. 该方法有效地使用未标记的数据来提高分类准确性,优于现有方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 膝关节关节炎 (OA) 是老年人残疾的主要原因,手动评估是主观和可变的.
- 目前用于OA分级的深度学习方法受到标签数据不足的限制,影响了性能和课程处理.
研究的目的:
- 开发一套全新的,全自动的凯尔格伦和劳伦斯 (KL) 级别分类系统,用于膝盖放射.
- 利用半监督多任务学习来提高KL级分类,使用标记和未标记的数据.
主要方法:
- 一个双通道的对抗性自动编码器被无人监督地训练为重建.
- 一个多任务学习框架包含了一个辅助的腿侧识别任务,以利用额外的数据集.
- 半监督学习利用未标记的数据来改善KL级分类的特征学习.
主要成果:
- 拟议的模型实现了精确度 (75.53%),精度 (74.1%),回忆 (78.51%) 和F1得分 (75.34%) 的最先进的性能.
- 使用未标记的数据和辅助任务显著提高了KL级分类性能.
- 该模型在两个大型公共数据集的评估中表现出了显著的稳定性.
结论:
- 开发的半监督多任务学习方法有效地解决了膝关节OA分级中有限的标记数据的挑战.
- 这种方法增强了特征学习,导致了优越的KL级分类性能和稳定性.
- 这些发现表明,在临床实践中对自动化,客观的OA评估有希望的方向.
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