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Breast ultrasound videos contain richer information than ultrasound images,therefore it is more meaningful to develop video models for this diagnosistask. However, the collection of ultrasound video datasets is much harder. Inthis paper, we explore the feasibility of enhancing the performance ofultrasound video classification using the static image dataset. To this end, wepropose KGA-Net and coherence loss. The KGA-Net adopts both video clips andstatic images to train the network. The coherence loss uses the feature centersgenerated 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 largemargin. The visualization results of frame attention prove the explainabilityof our method. The codes and model weights of our method will be made publiclyavailable.

Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers