Double Noise Mean Teacher Self-Ensembling Model for Semi-Supervised Tumor Segmentation

Accurate tumor segmentation of tumor images can assist doctors to diagnose diseases. However, achieving very high precision in tumor segmentation requires a large amount of annotated data, which is not easy for medical image data. In this paper, we present a novel double noise mean teacher self-ensembling model for semi-supervised 2D tumor segmentation. Concretely, the network is serialized by two groups of student-teacher networks. We design an auxiliary student-teacher module to learn the consistency regularity between the unlabeled image feature maps. In order to improve the robustness of the network, we add the random Gaussian noise to the student model every time the teacher model is updated. We test our model on the small cell lung tumor dataset and CVC-ClinicDB, and our model achieves the performance of nearly fully supervised segmentation. Moreover, the performance of our method outperforms the existing semi-supervised methods in four indicators.