Nodular lesion diagnosis based on ultrasound images plays an important role for ultrasound guided needle aspiration. However, it is a challenging task due to low signal-to-noise ratio, low contrast and so on. The research article published in journal Ultrasonics with title“A pre-trained convolutional neural network based method for thyroid nodule diagnosis”，proposes a hybrid method to classify thyroid nodules, which is a fusion of two pre-trained CNNs. The method is validated on 15,000 ultrasound images collected from two local hospitals and Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules.

The research article published in journal Medical Physics with title“Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images”，proposes a cascade CNN-based model to detect thyroid nodules. Results show that the proposed model significantly outperforms those methods on ultrasound thyroid nodule images, demonstrating its potential clinical applications. This technique can offer physicians an objective second opinion, and reduce their heavy workload so as to avoid misdiagnosis causes because of excessive fatigue. In addition, it is easy and reproducible for a person without medical expertise to diagnose thyroid nodules.