医学图像分割里标签噪声很头疼，这个基准套件专门测联邦学习下的真实场景，帮你挑最靠谱的去噪方法。
该基准套件整合了来自不同来源的真实噪声医学图像分割数据集，并设计了多种客户端-噪声场景（如轮廓不一致、缺失结构、标签混淆）。它提供了针对标签噪声的评估指标，支持系统性的联邦噪声标签学习（FNLL）评估。代码已在GitHub上开源（MIC-DKFZ/FedSegNoiseBench），为公平比较和未来方法开发奠定基础。
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels. Federated noisy label learning (FNLL) aims to mitigate these effects, yet remains underused in practice as existing evidence is largely based on synthetic noise, simplified settings, and limited real-world noisy evaluation. We address this gap by introducing a benchmark suite that combines diverse real-world noisy datasets, deployment-relevant client-noise scenarios, and label-noise-targeted evaluation to support systematic FNLL assessment and informed method selection. The suite combines curated real-world noisy medical image segmentation datasets from diverse sources with a comprehensive federated segmentation framework including various client-noise scenarios and noise-targeted evaluation. The presented suite provides a realistic and discriminative basis for FNLL evaluation in medical image segmentation and establishes a reusable foundation for fair benchmarking, dataset-specific label-noise characterization, and future method development under realistic federated settings. Code is available at https://github.com/MIC-DKFZ/FedSegNoiseBench.