DScGANS: Integrate Domain Knowledge in Training Dual-Path Semi-supervised Conditional Generative Adversarial Networks and S3VM for Ultrasonography Thyroid Nodules Classification

Semi-supervised learning can reduce the burden of manual label data and improve classification performance by learning with unlabelled data. However, due to the absence of label constraints, unlabelled data is usually ambiguous, which typically results in requiring large datasets to learn the correct feature space distribution. The inherently small sample characteristics of medical image datasets may make semi-supervised learning unstable, which may lead to mixed results and even degrade performance. The domain knowledge (DK) of the physician is of great value for disease diagnosis. In this paper, we propose to promote semi-supervised learning with DK and develop a DScGANS model (DScGAN (dual-path semi-supervised conditional generative adversarial networks) and S3VM (semi-supervised support vector machine)) to diagnose ultrasound thyroid nodules. DScGAN uses DK as a condition and multimodal ultrasound data for training. We concatenate the image representation of DScGAN learning and use it as the input of S3VM. DK will be used as a condition to constrain S3VM for thyroid nodule classification. The experimental results show that our proposed model can effectively avoid mixed results that may occur in semi-supervised learning with a small medical dataset with insufficient labels. Additionally, our model provides stable and advanced diagnostic performance and is potentially integrated into the thyroid ultrasound system.