No abstract

Objective: Thyroid nodules are common glandular abnormality that need to be diagnosed as benign or malignant to determine further treatments. Clinically, ultrasonography is the main diagnostic method, but it is highly subjective with severe variability. Recently, many deep-learning-based methods have been proposed to alleviate subjectivity and achieve good results yet, these methods often neglect important guidance from clinical knowledge. Our objective is to utilize such guidance for accurate and reliable thyroid nodule classification. Approach: In this study, a multi-task learning model embedded with clinical knowledge of ACR Thyroid Imaging, Reporting and Data System (TI-RADS) guideline is proposed. The clinical features defined in the guideline have strong correlations with malignancy and they were modeled as tasks alongside the pathological type. Multi-task learning was utilized to exploit the correlations to improve diagnostic performance. To alleviate the impact of noisy labels on clinical features, a loss-weighting strategy was proposed. Five-fold cross-validation was applied to an internal training set of size 4989, and an external test set of size 243 was used for evaluation. Main results: The proposed multi-task learning model achieved an average AUC of 0.901 and an ensemble AUC of 0.917 on the test set, which significantly outperformed the single-task baseline models. Significance: The results indicated that multi-task learning of clinical features can effectively classify thyroid nodules and reveal the possibility of using clinical indicators as auxiliary tasks to improve performance when diagnosing other diseases.