Semi-supervised Network for Thyroid Nodule Segmentation via Joint Consistency Learning and Co-Training

Li Dai, Guijuan Zhang, Lei Lyu

Abstract: Thyroid nodule is a common clinical disease, although most nodules are benign, the incidence rate of thyroid cancer has risen rapidly in recent years. Even though many methods have achieved automated thyroid nodule segmentation based on deep learning, these methods are based on supervised learning and require a large amount of labeled data for training. However, the labeling work must be carried out by professional doctors, which results in a small number of datasets and difficulty in labeling. To address this problem, this paper proposes a semi-supervised thyroid nodule segmentation model via joint consistency learning and co-training. This model includes two branches: consistency learning and co-training. In the consistency learning branch, based on consistent regularization, the teacher model guides the student model to optimize. In order to make the teacher model more stable, we design a co-training framework to further optimize the teacher model. In co-training branch, the teacher model and TransUNet extract different representations of the same sample and teach each other to prevent consistent but incorrect predictions between the teacher model and the student model. This semi-supervised model can learns useful feature representations from unlabeled data, and effectively trains the model with a small amount of labeled data, reducing the dependence on labeled data during the model training process.