An ensemble deep learning architecture for multilabel classification on TI-RADS

In recent years, thyroid nodule is being one of the most common nodular lesions. Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. With the development of artificial intelligence, there emerge great progress in medical image diagnosis. Clinically, physician diagnose malignant or benign by many pathological features. In this work, we propose an ensemble architecture to resolve multi-label problem, which integrate three methods to extract features on thyroid nodule for ultrasound images. They are EfficientNet, feature engineering and feature pyramid network. We consider five kinds of pathological features quantified by Thyroid Imaging Report and Data System (TI-RADS). In the experiments, we use two datasets. One includes 587 original ultrasound images on thyroid nodules collected from the local health physical center of a 3A hospital. The other is a public dataset in the MICCAI 2020 competition. It contains 3644 ultrasound images of thyroid images. We use receiver operating characteristic curve (ROC) to evaluate the model, the area under curve (AUC) on every kind of features. The experimental results show that the proposed method can effectively assist doctors in diagnosing ultrasound images of thyroid nodules.