  Breast ultrasound videos contain richer information than ultrasound images,
therefore it is more meaningful to develop video models for this diagnosis
task. However, the collection of ultrasound video datasets is much harder. In
this paper, we explore the feasibility of enhancing the performance of
ultrasound video classification using the static image dataset. To this end, we
propose KGA-Net and coherence loss. The KGA-Net adopts both video clips and
static images to train the network. The coherence loss uses the feature centers
generated by the static images to guide the frame attention in the video model.
Our KGA-Net boosts the performance on the public BUSV dataset by a large
margin. The visualization results of frame attention prove the explainability
of our method. The codes and model weights of our method will be made publicly
available.
