Thyroid Nodule Classification in Ultrasound Videos by Combining 3D CNN and Video Transformer

Diagnosing thyroid nodules with computer-aided techniques remains a challenging task. Using ultrasound videos for the classification of benign and malignant nodules can provide valuable timing and change information that is consistent with clinical diagnosis. In this paper, we propose a novel thyroid nodule classification model based on ultrasound video. To capture different semantic information, we sample video frames at various time intervals and extract local and global features using two different feature extraction branches. Our experimental results show that our method outperforms existing state-of-the-art methods, demonstrating its effectiveness in accurately diagnosing thyroid nodules with ultrasound videos.