pFLFE：通过特征增强实现医学图像分割的跨孤岛个性化联邦学习
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
AI总结：
针对医学图像分割中跨孤岛个性化联邦学习的客户端漂移问题，提出pFLFE框架，通过特征增强与监督学习两阶段设计及低通信成本训练策略，在三个医学分割任务上取得优于现有方法的性能。
摘要：
在医学图像分割中，个性化跨孤岛联邦学习（FL）因能够利用不同医疗机构间的多样数据以克服数据稀缺和隐私问题而日益受到关注。然而，现有方法常常遭受客户端漂移问题，导致性能不一致和训练延迟。我们提出了一种新的框架，即通过特征增强的个性化联邦学习（pFLFE），旨在缓解这些挑战。pFLFE包含两个主要阶段：特征增强和监督学习。第一阶段改善前景与背景特征之间的区分，第二阶段利用这些增强后的特征从分割掩码中学习。我们还设计了一种替代训练方法，在通信资源有限的情况下，能够减少通信轮次而不牺牲分割质量。通过在三个医学分割任务上的实验，我们证明pFLFE优于最先进的方法。
英文摘要：
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns. However, existing methods often suffer from client drift, leading to inconsistent performance and delayed training. We propose a new framework, Personalized Federated Learning via Feature Enhancement (pFLFE), designed to mitigate these challenges. pFLFE consists of two main stages: feature enhancement and supervised learning. The first stage improves differentiation between foreground and background features, and the second uses these enhanced features for learning from segmentation masks. We also design an alternative training approach that requires fewer communication rounds without compromising segmentation quality, even with limited communication resources. Through experiments on three medical segmentation tasks, we demonstrate that pFLFE outperforms the state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
