A Risk Stratification Study of Ultrasound Images of Thyroid Nodules Based on Improved DETR

The Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) standard is based on the Chinese current medical context. However, at present, there is a lack of C-TIRADS-based automatic computer-aided diagnosis system for thyroid nodule ultrasound images, and the existing algorithms for detecting and recognizing thyroid nodules are basically for the dichotomous classification of benign and malignant. We used the DETR (detection transformer) model as a baseline model and carried out model enhancements to address the shortcomings of unsatisfactory classification accuracy and difficulty in detecting small thyroid nodules in the DETR model. First, to investigate the method of acquiring multi-scale features of thyroid nodule ultrasound images, we choose TResNet-L as the feature extraction network and introduce multi-scale feature information and group convolution, thereby enhancing the model's multi-label classification accuracy. Second, a parallel decoder architecture for multi-label thyroid nodule ultrasound image classification is designed to enhance the learning of correlation between pathological feature class labels, aiming to improve the multi-label classification accuracy of the detection model. Third, the loss function of the detection model is improved. We propose a linear combination of Smooth L1-Loss and CIoU Loss as the model's bounding box loss function and asymmetric loss as the model's multi-label classification loss function, aiming to further improve the detection model's detection accuracy for small thyroid nodules. The experiment results show that the improved DETR model achieves an AP of 92.4% and 81.6% with IoU thresholds of 0.5 and 0.75, respectively.