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标签不准确性和图像噪声对全景放射图片中牙细分的影响,使用联邦,集中和本地学习

Johan Andreas Balle Rubak¹, Khuram Naveed¹, Sanyam Jain¹

¹Department of Dentistry and Oral Health, Aarhus University, Vennelyst Boulevard 9, 8000, Aarhus, Denmark.

Dento maxillo facial radiology

|January 17, 2026

概括

牙科人工智能牙细分的联合学习 (FL) 与数据腐败场景中的集中学习 (CL) 性能相匹配或超过. FL为可扩展的临床AI部署提供了一种实用,保护隐私的方法.

科学领域:

人工智能在牙科中的应用
机器学习用于医学成像
医疗保健中的数据隐私

背景情况:

联合学习 (FL) 对隐私问题,数据异质性和牙科AI中的标签不一致性提出了潜在的解决方案.
评估FL与集中学习 (CL) 和本地学习 (LL) 相比,对于理解其在现实临床环境中的有效性至关重要.

研究的目的:

为了比较联合学习 (FL) 与集中学习 (CL) 和局部学习 (LL) 的表现,在全景放射图中对牙进行细分.
在各种数据腐败场景下评估FL的稳定性,包括标签操纵,图像质量退化和错误的客户端排除.
调查每个客户损失轨迹在FL框架内用于异常检测的实用性.

主要方法:

一个注意力U-Net模型在6个机构的2066张X射线图上接受了培训.
模拟了四种数据腐败场景:基线,标签操纵,图像噪音和错误的客户端排除.
联合学习是使用Flower AI框架实现的,CL和LL作为比较器.
使用诸如Dice,IoU,HD,HD95和ASSD等指标评估绩效,通过Wilcoxon签名等级测试评估重要性.

主要成果:

联合学习 (FL) 在所有测试的数据腐败场景中始终匹配或超过集中学习 (CL) 的表现,包括标签操纵和图像噪声.
在大多数场景中,与本地学习 (LL) 相比,FL在大多数场景中表现优越,特别是在标签操纵下.
每个客户端的损失曲线监控有效地识别和标记了FL网络中的受损数据站点.

结论:

联合学习 (FL) 为牙科人工智能应用程序提供了一个强大的,保护隐私的中央学习 (CL) 替代方案,在数据腐败的情况下优于本地学习 (LL).
每个客户端损失监控的整合增强了异常检测,支持FL作为可扩展和安全的临床AI部署的实际解决方案.

关键词:

云计算云计算云计算云计算数据异质性数据异质性深度学习 (Deep Learning) 是一种深度学习.牙科放射学牙科放射学联邦学习学习 (Federated Learning) 是一种学习方式.花 ai ai 的花朵图像细分图像细分

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