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因医学图像标注成本高昂而备受关注。本文提出一种新颖的固有一致性学习（ICL）方法，旨在通过标记与未标记数据的语义一致性引导，学习稳健的语义类别表征以辅助分割。实际应用中，我们引入两个基于注意力机制的外部模块：监督语义代理适配器（SSPA）与无监督语义一致性学习器（USCL），分别用于对齐标记与未标记数据的语义类别表征，并更新整个训练集上的全局语义表征。所提ICL是一种适用于多种网络架构的即插即用方案，且两个模块不参与测试阶段。在三个公开基准数据集上的实验结果表明，该方法尤其在标注数据极度有限时能够超越现有最优技术。代码开源地址：https://github.com/zhuye98/ICL.git。