One-stage Framework for Thyroid Nodule Detection with Mixup and Negative Sample Utilization

Ultrasound imaging plays a significant role in the early diagnosis of thyroid nodules. However, ambiguous nodule boundaries and their similarity to surrounding tissue cause challenges. These cause high false positive rates, reducing the diagnostic efficiency of ultrasound imaging. Although recent advancements in deep learning such as Faster R-CNN and YOLO, have shown progress, they still have limitations in handling imbalanced datasets and noise during real-time detection. To solve these challenges, our team proposes a novel deep learning framework integrating mixup data augmentation and negative sample utilization. This approach reduces false positives while maintaining a stable true positive detection rate. The method employs a one-stage-based object detection model as the baseline. The proposed framework based on that can improve the model’s robustness against background noise and optimize mixup parameters to enhance generalization. The experimental results demonstrate our approach significantly reduces false positive rates by 40% (from 50% to below 10%). In the meantime, it can stabilize high true positive rates above 80% in thyroid nodule detection during testing. This improvement highlights the framework can balance diagnostic sensitivity and specificity. The framework provides an efficient solution for detecting nodules in thyroid ultrasound imaging and has great potential to enhance diagnostic accuracy.