While supervised learning has achieved remarkable success, obtaining large-scale labeled datasets in biomedical imaging is often impractical due to high costs and the time-consuming annotations required from radiologists. Semi-supervised learning emerges as an effective strategy to overcome this limitation by leveraging useful information from unlabeled datasets. In this paper, we present a novel semi-supervised learning method, Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA), for medical image segmentation. We devise a consistency regularization to promote consistent representations during the training process. Specifically, we use distinct decoders for student and teacher networks while maintain the same encoder. Moreover, to learn from unlabeled data, we create pseudo-labels generated by the teacher networks and augment the training data with the pseudo-labels. Both techniques contribute to enhancing the performance of the proposed method. The method is evaluated on three representative medical image segmentation datasets. Comprehensive comparisons with state-of-the-art semi-supervised medical image segmentation methods were conducted under typical scenarios, utilizing 10% and 20% labeled data, as well as in the extreme scenario of only 5% labeled data. The experimental results consistently demonstrate the superior performance of our method compared to other methods across the three semi-supervised settings. The source code is publicly available at https://github.com/BinYCn/DCPA.git.

翻译：尽管监督学习已取得显著成功，但在生物医学成像中获取大规模标注数据集往往因成本高昂和放射科医生耗时的手工标注而不可行。半监督学习通过利用未标注数据集中的有用信息，成为克服这一局限的有效策略。本文提出了一种新型半监督学习方法——基于伪标签引导数据增强的双解码器一致性方法（DCPA），用于医学图像分割。我们设计了一种一致性正则化方法，在训练过程中促进表征的一致性。具体而言，我们为学生网络和教师网络采用不同的解码器，同时保持编码器相同。此外，为从无标注数据中学习，我们利用教师网络生成的伪标签增强训练数据。这两种技术共同提升了所提方法的性能。该方法在三个具有代表性的医学图像分割数据集上进行了评估。在典型场景（使用10%和20%标注数据）以及仅含5%标注数据的极端场景下，与当前最先进的半监督医学图像分割方法进行了全面比较。实验结果表明，在三种半监督设置下，我们的方法均持续展现出优于其他方法的性能。源代码已公开于https://github.com/BinYCn/DCPA.git。