FedA3I：面向异构标注噪声的标注质量感知联邦医学图像分割聚合方法
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
AI总结：
本文首次针对联邦医学图像分割中跨客户端异构标注噪声问题，提出FedA3I方法，通过高斯混合模型估计客户端噪声并以逐层质量加权聚合提升鲁棒性，在两个真实数据集上验证了其优越性能。
摘要：
联邦学习（FL）因其隐私保护特性，已成为在分散的医学数据上训练分割模型的一种有前景的范式。然而，现有研究忽视了现实世界医学数据集中普遍存在的标注噪声，这限制了联邦学习的性能上限。在本文中，我们首次识别并解决这一问题。在问题建模方面，我们提出一种轮廓演化方法来建模每个客户端内部像素间非独立同分布（Non-IID）的噪声，并将其扩展到多源数据情形，从而形成异构噪声模型（即跨客户端的非独立同分布标注噪声）。为了从具有这种两级非独立同分布噪声的标注中实现鲁棒学习，我们强调数据质量在模型聚合中的重要性，使高质量客户端能够对联邦学习产生更大的影响。为此，我们提出了一种具有标注质量感知聚合的联邦学习方法，命名为FedA3I，通过引入基于客户端级噪声估计的质量因子来实现。具体而言，每个客户端的噪声估计通过高斯混合模型完成，然后以逐层方式融入模型聚合中，以提高高质量客户端的权重。在两个真实世界医学图像分割数据集上的大量实验表明，FedA3I在处理跨客户端标注噪声方面优于最先进的方法。代码可在https://github.com/wnn2000/FedAAAI获取。
英文摘要：
Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, existing research overlooks the prevalent annotation noise encountered in real-world medical datasets, which limits the performance ceilings of FL. In this paper, we, for the first time, identify and tackle this problem. For problem formulation, we propose a contour evolution for modeling non-independent and identically distributed (Non-IID) noise across pixels within each client and then extend it to the case of multi-source data to form a heterogeneous noise model (i.e., Non-IID annotation noise across clients). For robust learning from annotations with such two-level Non-IID noise, we emphasize the importance of data quality in model aggregation, allowing high-quality clients to have a greater impact on FL. To achieve this, we propose Federated learning with Annotation quAlity-aware AggregatIon, named FedA3I, by introducing a quality factor based on client-wise noise estimation. Specifically, noise estimation at each client is accomplished through the Gaussian mixture model and then incorporated into model aggregation in a layer-wise manner to up-weight high-quality clients. Extensive experiments on two real-world medical image segmentation datasets demonstrate the superior performance of FedA$^3$I against the state-of-the-art approaches in dealing with cross-client annotation noise. The code is available at https://github.com/wnn2000/FedAAAI.
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。
