A Tumor Segmentation Method Based on Mean-Teacher Reusing Pseudo-Labels

Abstract

Breast tumor is a common female physiological disease, and the malignant tumor is one of the main fatal diseases of women. Accurate examination and assessment of tumor shape can facilitate subsequent treatment and improve the cure rate. With the development of deep learning, automatic detection systems are designed to assist doctors in diagnosis. However, the blurry edges, poor visual quality, and irregular shapes of breast tumors pose significant challenges to design a highly efficient detection system. In addition, the lack of publicly available labeled data is a major obstacle in developing highly accurate and robust deep learning models for breast tumor detection. To overcome the aforementioned issues, we propose SRU-PMT+, a pseudo-label reusing Mean-Teacher architecture based on squeeze-and-excitation residual (SE-Res) attention. We utilize the proposed segmentation network, SRU-Net++, to generate pseudo-labels for unlabeled data, and guide the learning of the student model using the generated pseudo-labels and groundtruth, improving the accuracy and robustness of the model. Our proposed semi-supervised method has been rigorously evaluated on the available labeled dataset, i.e., Breast Ultrasound Images (BUSI) dataset. Results show that our proposed method outperforms current segmentation methods and has good performance. Importantly, our strategy of reusing pseudo-labels improves the performance of breast tumor segmentation.

Keywords

• Semi-supervised learning
• mean-teacher
• ultrasound images
• tumor segmentation