Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, which aims to learn robust semantic category representations through the semantic consistency guidance of labeled and unlabeled data to help segmentation. In practice, we introduce two external modules namely Supervised Semantic Proxy Adaptor (SSPA) and Unsupervised Semantic Consistent Learner (USCL) that based on the attention mechanism to align the semantic category representations of labeled and unlabeled data, as well as update the global semantic representations over the entire training set. The proposed ICL is a plug-and-play scheme for various network architectures and the two modules are not involved in the testing stage. Experimental results on three public benchmarks show that the proposed method can outperform the state-of-the-art especially when the number of annotated data is extremely limited. Code is available at: https://github.com/zhuye98/ICL.git.
翻译:半监督医学图像分割近年来受到了广泛关注,因为医学图像注释的成本很高。在本文中,我们提出了一种新的基于一致性学习的方法(ICL),通过标记和未标记数据的语义一致性引导来学习强大的语义类别表示,从而帮助分割。在实践中,我们引入了两个外部模块,即监督语义代理适配器(SSPA)和无监督语义一致性学习器(USCL),这两个模块基于注意力机制来对齐标记和未标记数据的语义类别表示,以及更新整个训练集的全局语义表示。所提出的ICL是一种可插拔的方案,适用于各种网络架构,两个模块不涉及测试阶段。在三个公共基准上的实验结果表明,所提出的方法在标注数据数量极少的情况下可以超过最先进的方法。可在 https://github.com/zhuye98/ICL.git 上获取代码。