  Limited labeled data hinder the application of deep learning in medical
domain. In clinical practice, there are sufficient unlabeled data that are not
effectively used, and semi-supervised learning (SSL) is a promising way for
leveraging these unlabeled data. However, existing SSL methods ignore frequency
domain and region-level information and it is important for lesion regions
located at low frequencies and with significant scale changes. In this paper,
we introduce two consistency regularization strategies for semi-supervised
medical image segmentation, including frequency domain consistency (FDC) to
assist the feature learning in frequency domain and multi-granularity region
similarity consistency (MRSC) to perform multi-scale region-level local context
information feature learning. With the help of the proposed FDC and MRSC, we
can leverage the powerful feature representation capability of them in an
effective and efficient way. We perform comprehensive experiments on two
datasets, and the results show that our method achieves large performance gains
and exceeds other state-of-the-art methods.
