FedFTN:个性化联合学习与深度特征转换网络,用于多机构的低数PET拒绝使用
Bo Zhou1, Huidong Xie1, Qiong Liu1
1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Medical image analysis
|October 12, 2023
概括
FedFTN通过在各个机构实现个性化拒绝来增强低计数正子发射断层扫描 (PET) 成像. 这种联合学习方法可以提高图像质量,而无需共享敏感的患者数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 低计数PET成像减少了辐射暴露,但产生了较低的信号噪声比 (SNR) 图像,阻碍了诊断准确性.
- 训练用于PET图像增强的深度学习模型是具有挑战性的,因为数据隐私,安全性和分布在各机构之间存在差异.
研究的目的:
- 开发一个个性化的联合学习策略,FedFTN,用于在多个机构中有效的低数PET图像否定.
- 为了解决多机构低数量的PET中使用联合学习的领域转移问题.
主要方法:
- FedFTN使用本地深度特征转换网络 (FTN) 来个性化全球共享的网络.
- 采用联合学习,只传达和聚合网络权重,而FTN 则保持本地化.
主要成果:
- 在三大洲,FedFTN成功生成了高质量的低计数PET图像.
- 拟议的方法在所有测试的低数级和机构中超越了现有的联合学习重建方法.
结论:
- 在多机构环境中,FedFTN提供了一种强大而个性化的解决方案,用于在多机构环境中进行低数量的PET图像染.
- 这种方法有效地处理数据异质性,并在协作医学成像研究中保护患者的隐私.
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