通过区域协同监督提升伪标签质量，有效缓解医学图像分割中的噪声问题。
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
- 基于均值教师框架，用混合增强策略优化未标注数据。
- 根据增强前后的协同效应划分伪标签区域，实现精细化监督。
- 在LA数据集上优于现有方法，适合高精度医学图像分割任务。
半监督学习因其能利用大量未标注数据提升模型鲁棒性而受到广泛关注。伪标签是半监督学习中常用策略，但现有方法常受噪声污染影响，损害模型性能。为此，本文提出一种新型的协同引导区域伪标签监督框架（SGRS-Net）。该框架基于均值教师网络，引入混合增强模块以提升未标注数据质量。通过评估增强前后标签的协同效应，将伪标签智能划分为不同区域。同时，设计区域损失评估模块，对各区域损失进行精细化分析。在LA数据集上的大量实验表明，本方法显著优于当前最先进技术，验证了其高效性与实用性。
原文摘要 · Abstract (English)
Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework.
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