Semi-supervised classification of medical ultrasound images based on generative adversarial network

Medical ultrasound (US) is one of the most widely used imaging modalities in clinical practice. However, its use presents unique challenges such as variable imaging quality. Deep learning (DL) can be used as an advanced medical US images analysis tool, while the performance of the DL model is greatly limited by the scarcity of big datasets. Here, we develop semi-supervised classification enhancement (SSCE) structures by constructing seven convolutional neural network (CNN) models and one of the most state-of-the-art generative adversarial network (GAN) models, StyleGAN2-ADA, to address this problem. A breast cancer dataset with 780 images is used as our base dataset. The results show that our SSCE structures obtain an accuracy of up to 97.9%, showing a maximum 21.6% improvement compared with utilizing CNN models alone and outperforming the previous methods using the same dataset by up to 23.9%. We believe our proposed state-of-the-art method can be regarded as a potential auxiliary tool for on-the-fly diagnoses of medical US images.