通过变压器进行联邦域调整,用于多站点阿尔茨海默病诊断
IEEE transactions on medical imaging
|August 1, 2023
概括
联合学习和变换器网络解决了多站点阿尔茨海默病 (AD) 研究中的数据隐私和异质性. FedDAvT框架有效地提高了各种数据集上的模型性能,提高了AD诊断的准确性.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 多地点阿尔茨海默病 (AD) 研究面临的挑战是由于数据异质性,降低了目标地点的模型性能.
- 传统的域名适应方法通过要求数据共享,引发了数据隐私问题.
- 联合学习为分布式多站点数据集上的培训模型提供了一种保护隐私的方法.
研究的目的:
- 提出使用变压器网络的新型多站点联合域调整框架 (FedDAvT).
- 在阿尔茨海默病研究中解决数据隐私和异质性问题.
- 提高多站点AD数据集中的预测模型的性能.
主要方法:
- 开发了一个名为FedDAvT的多站点联合域名适应框架.
- 利用变压器网络作为提取感兴趣区域特征及其相关性的骨干.
- 在源域和目标域之间对齐的自我注意地图,使用平均平方误差来适应子域.
主要成果:
- FedDAvT有效地保护数据隐私,同时减轻数据异质性.
- 取得的高准确率为88.75% (AD与NC),69.51% (MCI与NC) 和69.88% (AD与MCI).
- 在多站点阿尔茨海默病数据集上表现出显著的有效性.
结论:
- 拟议的FedDAvT框架是多站点AD研究中保护隐私的域调整的高效解决方案.
- 变压器网络擅长捕获复杂的大脑信息和相关性,以改善分类.
- FedDAvT提供了一种有前途的方法,可以在各种数据源中推进阿尔茨海默病的诊断和研究.
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