Cross-Mix Monitoring for Medical Image Segmentation With Limited Supervision

Abstract

Image segmentation is a fundamental building block of automatic medical applications. It has been greatly improved since the emergence of deep neural networks. However, deep-learning-based models often require a large amount of manual annotations, which has seriously hindered their practical usage. To alleviate this problem, numerous works were proposed by utilizing unlabeled data based on semi-supervised frameworks. Recently, the Mean-Teacher (MT) model has been successfully applied in many scenarios due to its effective learning strategy. Nevertheless, the existing MT model still has certain limitations. Firstly, to gain extra generalization ability through consistency training, various sorts of perturbations are often added to the training data. However, if the variation is too weak, it may cause the Lazy Student Phenomenon, and cause fluctuations in the learning model. On the contrary, large image perturbations may enlarge the performance gap between the teacher and student. In this case, the student may lose its learning momentum, and more seriously, drag down the overall performance of the whole system. In order to address these issues, we introduce a novel semi-supervised medical image segmentation framework, in which a Cross-Mix Teaching paradigm is proposed to provide extra data flexibility, thus effectively avoid Lazy Student Phenomenon. Moreover, a lightweight Transductive Monitor is applied to serve as the bridge that connects the teacher and student for active knowledge distillation. In the light of this cross-network information mixing and transfer mechanism, our method is able to continuously explore the discriminative information contained in unlabeled data. Extensive experiments on challenging medical image data sets demonstrate that our method outperforms current state-of-the-art semi-supervised segmentation methods under severe lack of supervision.