垂直联合学习中的自适应多模式融合,用于分散的玻璃眼查.
Ayesha Jabbar1,2, Jianjun Huang1,2, Muhammad Kashif Jabbar1,2
1College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
Brain sciences
|September 27, 2025
概括
这项研究引入了一种新的质量意识的垂直联合学习 (QAVFL) 框架,用于使用多式联络数据进行分散的青光眼检测. 在检测青光眼的同时,QAVFL在分散的医疗保健系统中保持了患者的隐私.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 早期发现青光眼对于预防视力丧失至关重要.
- 单模式视网膜成像在准确性和上下文方面存在局限性.
- 碎片化的临床数据和隐私法规阻碍了集中的AI开发.
研究的目的:
- 开发一个去中心化的多模式眼的检测框架.
- 为了应对青光眼诊断中的数据稀疏性和隐私挑战.
- 为了提高青光眼查的准确性和稳定性.
主要方法:
- 提出了一个质量意识的垂直联合学习 (QAVFL) 框架.
- 综合临床文本,视网膜图像和生物医学信号,使用特定模式编码器.
- 采用融合注意模块 (FAM) 进行自适应多模式加权.
- 利用同态加密和差异隐私来实现安全的聚合.
主要成果:
- QAVFL实现了98.6%的准确性,98.6%的回忆,97.0%的F1得分和0.992的AUC.
- 在统计学上显著地改善了单模和早期融合方法 (p < 0.01).
- 在异质的非IID设置中验证的性能.
结论:
- 动态多式联络融合对于保护隐私的分散学习是有效的.
- QAVFL提供了一个可扩展和临床适用的解决方案,用于眼查.
- 该框架解决了针对人工智能驱动诊断的医疗保健数据碎片化的挑战.
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