  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, 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 is 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.
