医疗联合学习与联合图形净化,用于噪音标签学习
Zhen Chen1, Wuyang Li2, Xiaohan Xing3
1Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong Special Administrative Region of China.
Medical image analysis
|October 8, 2023
概括
联合学习 (FL) 面临着医学成像中的标签噪声挑战. 拟议的FedGP框架使用图形净化和全球中心点聚合来提高诊断模型的准确性和隐私在杂的,分散的数据集.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 能够实现协作模式培训,同时保持数据隐私,这对于敏感的医疗数据至关重要.
- 标签噪声,由于观察者变化,是医疗数据集准备的固有挑战.
- 由于数据无法访问和客户之间潜在的噪声异质性,FL加剧了标签噪声问题.
研究的目的:
- 开发一个强大的联合学习框架,解决医学成像中的标签噪声问题.
- 为了提高诊断模型的性能,尽管固有的数据缺陷和隐私限制.
主要方法:
- 拟议的FedGP框架与客户端噪音图形净化,可靠的伪标签生成.
- 实施了以图形为导向的负集体损失,以便对标签噪声进行强有力的监督.
- 引入了服务器端的全球中心聚合,用于协作优化和强大的全球知识集成.
主要成果:
- 在医疗FL环境中,FedGP显著优于现有方法,具有同质,异质和现实世界的标签噪声.
- 在内镜和病理图像数据集上表现出卓越的性能.
- 在最先进的无声和杂的FL技术上取得了实质性的改进.
结论:
- 该 FedGP 框架有效地减轻了医疗成像联合学习中的标签噪声挑战.
- 联合图形净化和全球聚合为保护隐私,准确的医疗AI提供了强大的解决方案.
- 拟议的方法为FL在医疗保健中的实际应用提供了显著的进步.
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