Though supervised learning gains impressive success, the acquisition of indispensable large-scale labeled datasets are often impractical in biomedical imaging partially due to expensive costs and lengthy annotations done by experienced radiologists. Semi-supervised learning has been shown to be an effective way to address this limitation by leveraging useful information from unlabeled datasets. In this paper, we present a new semi-supervised learning method referred to as Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA) for medical image segmentation. We devise a consistency regularization to improve the semi-supervised learning. Specifically, to promote consistent representations during the training process, we use different decoders for student and teachers 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. The two techniques contribute to the improved performance of the proposed method. We evaluate the performance of the proposed method on three representative medical image segmentation datasets. Extensive comparisons to the state-of-the-art medical image segmentation methods were carried out under typical scenarios with 10% and 20% labeled data. Experimental outcomes demonstrate that our method consistently outperforms state-of-the-art semi-supervised medical image segmentation methods over the three semi-supervised settings. Furthermore, to explore the performance of proposed method under extreme condition, we conduct experiments with only 5% labeled data. The results further verify the superior performance of the proposed method. Source code is publicly online at https://github.com/BinYCn/DCPA.git.

翻译：尽管监督学习取得了令人瞩目的成功，但在生物医学成像中获取大规模标注数据集往往不切实际，部分原因是资深放射科医生标注成本高昂且耗时。半监督学习已被证明是解决这一局限的有效途径，能够利用未标注数据中的有用信息。本文提出一种名为"通过伪标签引导数据增强的双解码器一致性"（DCPA）的半监督学习方法，用于医学图像分割。我们设计了一种一致性正则化方法以提升半监督学习性能。具体而言，在训练过程中为促进学生网络和教师网络保持一致的表示，我们采用不同解码器但共享相同编码器的架构。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扩充训练数据。这两种技术共同提升了所提方法的性能。我们在三个代表性医学图像分割数据集上评估了该方法，并在典型场景下（10%和20%标注数据）与最新医学图像分割方法进行了广泛对比。实验结果表明，在三种半监督设置下，我们的方法始终优于现有最优的半监督医学图像分割方法。为探索极端条件下的性能，我们进一步开展了仅使用5%标注数据的实验，结果验证了所提方法的优越性。源代码已公开于 https://github.com/BinYCn/DCPA.git。