通过分步优化提升脑部MRI分割的半监督学习效果
DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization
- 双分支异步优化，分别训练编码器和解码器
- 在ISLES2022和BraTS上性能超越现有方法
- 适合标注数据少、噪声高的医学图像分割场景
医学影像中标注数据稀缺，半监督学习因其能利用不完整标注而愈发重要。尽管教师-学生框架表现良好，但联合优化网络常导致收敛困难与不稳定。为此，我们提出DuetMatch，一种基于解耦分支优化的新型双分支半监督框架，其中每个分支分别优化编码器或解码器，同时冻结另一部分。为增强噪声条件下的一致性，引入解耦丢弃扰动以实现跨分支正则化；设计成对剪切混合交叉引导机制，通过增强输入对交换伪标签以提升模型多样性。为缓解噪声伪标签引发的确认偏差，提出一致性匹配策略，利用冻结教师模型的稳定预测进行标签修正。在包含ISLES2022和BraTS在内的多个基准脑部MRI分割数据集上的大量实验表明，DuetMatch持续优于当前最优方法，展现出在多种半监督分割场景下的有效性与鲁棒性。
原文摘要 · Abstract (English)
The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, teacher-student frameworks have gained popularity for their training benefits and robust performance. However, jointly optimizing the entire network can hinder convergence and stability, especially in challenging scenarios. To address this for medical image segmentation, we propose DuetMatch, a novel dual-branch semi-supervised framework with asynchronous optimization, where each branch optimizes either the encoder or decoder while keeping the other frozen. To improve consistency under noisy conditions, we introduce Decoupled Dropout Perturbation, enforcing regularization across branches. We also design Pair-wise CutMix Cross-Guidance to enhance model diversity by exchanging pseudo-labels through augmented input pairs. To mitigate confirmation bias from noisy pseudo-labels, we propose Consistency Matching, refining labels using stable predictions from frozen teacher models. Extensive experiments on benchmark brain MRI segmentation datasets, including ISLES2022 and BraTS, show that DuetMatch consistently outperforms state-of-the-art methods, demonstrating its effectiveness and robustness across diverse semi-supervised segmentation scenarios.
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