Semi-supervised learning for breast-ultrasound image
Menghua Zhang, Guowu Yang, Defu Liu, Jinzhao Wu · 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022) · 2022
Recent deep learning models developed to address classification problems related to medical imaging for diagnosis focus on supervised models. However, supervised models are highly dependent on a large amount of accurately labeled training data to train the models. Obtaining such accurately labeled training data is expensive, time-consuming, and requires human expertise, which challenges the generalizing ability of deep learning models in many medical fields. To address this problem, we propose MeanMatch, a semi-supervised method that use a limited amount of labeled data and many unlabeled data to train deep models. We also employ the voting technical to assemble the deep models to improve the generalization performance. Finally, we conduct experiments on the breast-ultrasound image dataset to verify the effectiveness of our approach and compare it to the current state-of-the-art semi-supervised methods. Experimental results show that our method achieves the highest classification accuracy of 83.0\% on the test set, which outperforms or is comparable to the comparison methods and the supervised models.