Reciprocal Learning for Semi-supervised Segmentation

Semi-supervised learning has been recently employed to solve problems from medical image segmentation due to challenges in acquiring sufficient manual annotations, which is an important prerequisite for building high-performance deep learning methods. Since unlabeled data is generally abundant, most existing semi-supervised approaches focus on how to make full use of both limited labeled data and abundant unlabeled data. In this paper, we propose a novel semi-supervised strategy called reciprocal learning for medical image segmentation, which can be easily integrated into any CNN architecture. Concretely, the reciprocal learning works by having a pair of networks, one as a student and one as a teacher. The student model learns from pseudo label generated by the teacher. Furthermore, the teacher updates its parameters autonomously according to the reciprocal feedback signal of how well student performs on the labeled set. Extensive experiments on two public datasets show that our method outperforms current state-of-the-art semi-supervised segmentation methods, demonstrating the potential of our strategy for the challenging semi-supervised problems. The code is publicly available at https://github.com/XYZach/RLSSS.