  Breast lesion detection in ultrasound video is critical for computer-aided
diagnosis. However, detecting lesion in video is quite challenging due to the
blurred lesion boundary, high similarity to soft tissue and lack of video
annotations. In this paper, we propose a semi-supervised breast lesion
detection method based on temporal coherence which can detect the lesion more
accurately. We aggregate features extracted from the historical key frames with
adaptive key-frame scheduling strategy. Our proposed method accomplishes the
unlabeled videos detection task by leveraging the supervision information from
a different set of labeled images. In addition, a new WarpNet is designed to
replace both the traditional spatial warping and feature aggregation operation,
leading to a tremendous increase in speed. Experiments on 1,060 2D ultrasound
sequences demonstrate that our proposed method achieves state-of-the-art video
detection result as 91.3% in mean average precision and 19 ms per frame on GPU,
compared to a RetinaNet based detection method in 86.6% and 32 ms.
