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针对小儿肺炎的高效联合学习在胸部X射线分类上

Zegang Pan¹, Haijiang Wang^2,3, Jian Wan^4,5

¹School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China.

Scientific reports

|October 7, 2024

概括

联合学习增强了从胸部X射线的儿科肺炎诊断,提高了准确性,同时保护了患者数据隐私. 这种方法解决了数据异质性,实现了提高临床使用的高检测率.

科学领域:

医学成像分析分析医学成像分析
医疗保健中的人工智能
机器学习中的数据隐私

背景情况:

全球范围内,肺炎是5岁以下儿童死亡的主要原因.
对于胸部X射线诊断的传统机器学习面临着由于数据集中而导致的数据隐私和安全挑战.
联合学习提供了一个解决方案,通过培训模型而无需共享敏感的患者数据.

研究的目的:

开发一种联合学习方法,通过胸部X射线准确和安全地诊断儿科肺炎.
为了应对影响分类性能的联合学习中的数据异质性挑战.
改进现有的基于联合学习和扩散模型的方法,用于儿童肺炎检测.

主要方法:

利用联合学习 (FL),对FedAvg算法进行修改.
修改了客户端损失函数,将常规或罚款条款纳入其中.
在处理数据异质性的平均聚合后实现的服务器端动量.
采用双端控制变量方法,以减轻分类性能恶化.

主要成果:

拟议的联合学习方法有效地保护数据安全,防止隐私泄露.
在儿科肺炎分类方面,平均准确率达到98%,个别准确率高达99%.
与以前的联合学习算法和扩散模型相比,显示了平均2%的改进.
成功解决了影响分类准确性的数据异质性问题.

结论:

联合学习为医疗图像分析提供了传统机器学习的安全替代方案.
拟议的双端控制变量方法在异质联合学习环境中显著提高了分类准确性.
这种方法为世界各地的临床医生提供了一种有价值的工具,以提高儿童肺炎的检测.

关键词:

儿童肺炎的胸部X射线分类在两端的控制变量.数据异质性数据异质性联合学习是联合学习.

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