A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos

TL;DR

This paper introduces a new annotated ultrasound video dataset for breast lesion detection and proposes CVA-Net, a novel model that effectively aggregates clip-level and video-level features to improve detection accuracy.

Contribution

The paper provides the first annotated ultrasound video dataset for breast lesion detection and develops CVA-Net, a new model that combines local and global temporal features for enhanced detection performance.

Findings

CVA-Net outperforms existing methods on the new dataset.

The dataset contains 188 annotated ultrasound videos.

Video-level features improve detection accuracy.

Abstract

Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 videos) for breast lesion detection. Moreover, we propose a clip-level and video-level feature aggregated network (CVA-Net) for addressing breast lesion detection in ultrasound videos by aggregating video-level lesion classification features and clip-level temporal features. The clip-level temporal features encode local temporal information of ordered video frames and global temporal information of shuffled video frames. In our CVA-Net, an inter-video fusion module is devised to fuse local features from original video frames and global features from shuffled video frames, and an intra-video fusion module is devised to learn the temporal information among adjacent video frames. Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos. Experimental results on our annotated dataset demonstrate that our CVA-Net clearly outperforms state-of-the-art methods. The corresponding code and dataset are publicly available at \url{https://github.com/jhl-Det/CVA-Net}.