A Multi-Branch Feature Fusion Transformer Network and Its Application in Neck Ultrasound Detection.

Hyperparathyroidism (HPT) and thyroid nodule (TN) are caused by abnormalities in the parath/yroid and thyroid glands, respectively. Due to their proximity, small size, and similar ultrasound characteristics, the traditional object detection algorithms often struggle to accurately differentiate between HPT and TN when both lesions coexist in ultrasound images, leading to a high rate of misdiagnosis. In order to achieve accurate detection of HPT and TN when the two lesions coexist, we constructed three comprehensive object detection datasets: one containing only hyperparathyroidism (HPTD), one containing only thyroid nodules (TND), and one mixed dataset that includes both types of lesions (HPT-TND). A novel multi-branch feature fusion DETR network (MB-DETR) is proposed based on the Real-Time Detection Transformer (RT-DETR) model. We redesigned the feature fusion module and incorporated asymmetric convolution to enhance feature extraction. To validate the proposed MB-DETR performance, the experiments have been carried out on the three datasets. Our model achieved a superior performance compared to the state-of-the-art object detection models in the key metrics such as F1, Precision, and Recall, while significantly reducing computational costs. Additionally, the ablation studies confirmed the effectiveness of asymmetric convolution and the multi-branch feature fusion module in terms of enhancement of detection performance. The experimental results show that the Multi-Branch Feature Fusion incorporated with the asymmetric convolution improves the local feature extraction capability of the DETR model. It is concluded that the proposed MB-DETR model outperforms the existing ones in the detection of TN and HPT when both lesions coexist and thus effectively assists in the diagnosis of the correlated disease.