A proposal-level class-aware graph convolutional network and memory bank for thyroid nodule detection in ultrasound videos

Abstract

Thyroid nodule detection in ultrasound is an effective method for early thyroid cancer diagnosis and can significantly reduce the workload of radiologists. However, this technology still faces considerable challenges due to issues such as low image quality, image artifacts, and speckle noise in ultrasound images. In this paper, we design a new framework for thyroid nodule detection, which enhances low-quality features in ultrasound videos for addressing thyroid nodule detection in ultrasound videos by leveraging high-quality features. In our framework, we propose a novel proposal-level class-aware graph convolutional network module, which removes noise interference from different classes and effectively utilizes temporal information from multiple frames to improve feature representation of the current frames. Furthermore, to further enhance the detection capability of the network for thyroid nodules in ultrasound videos, we design a new proposal-level memory bank to store and update high-quality proposal features in ultrasound videos. By fusing high-quality features from the memory bank with features of the current frames, our approach enables the enhancement of low-quality features in the current frames, thereby improving the performance of the network. Experimental results demonstrate that our proposed framework achieves a significant improvement over the previous state-of-the-art methods and superior real-time inference speed on our collected ultrasound thyroid video dataset.