UFPS:在异质数据分布中进行部分注释的联合细分的统一框架
Le Jiang1, Li Yan Ma1, Tie Yong Zeng2
1School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Patterns (New York, N.Y.)
|February 19, 2024
概括
联邦部分监督细分处理医疗成像中的隐私和数据问题. 我们的UFPS框架统一了标签学习和特征空间,提高了细分的准确性和概括性.
科学领域:
- 医学图像分析 医学图像分析
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 部分监督细分为具有不完整注释的医疗数据集提供了标签效率高的方法.
- 由于数据隐私问题和异质性,现实世界的部署面临挑战,限制了实际应用.
- 联合学习可以在不共享原始数据的情况下进行协作模型培训,从而保护隐私.
研究的目的:
- 引入联邦部分监督细分 (FPSS) 以克服隐私和数据异质性的障碍.
- 提出一个统一的框架 (UFPS),解决FPSS中的类异质性和客户端漂移问题.
- 为了在保护隐私的环境中,在部分注释的医疗数据集中准确地对所有类别进行细分.
主要方法:
- 开发了统一的联邦部分标记分段 (UFPS) 框架.
- 集成的统一标签学习 (ULL) 防止类碰撞,并确保全面的像素细分.
- 利用Sparse统一的度意识最小化 (sUSAM) 来实现功能空间统一,并对抗客户端漂移.
主要成果:
- 经验研究表明,传统的联合学习和部分监督的方法在结合时会遭受阶级碰撞.
- 与现有方法相比,UFPS框架展示了优越的冲突解决能力.
- 在真实的医疗数据集上进行了广泛的实验,验证了UFPS增强的泛化性能.
结论:
- 拟议的UFPS框架有效地解决了在联邦部分监督细分中对类异质性和客户漂移的挑战.
- UFPS提供了一个强大的解决方案,用于使用部分注释数据来保护隐私的医疗图像细分.
- 该框架显示了对现实世界医疗数据集的细分精度和概括性的显著改进.
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