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Deep learning based Computer Aided Diagnosis (CAD) systems have beendeveloped to treat breast ultrasound. Most of them focus on a single ultrasoundimaging modality, either using representative static images or the dynamicvideo of a real-time scan. In fact, these two image modalities arecomplementary for lesion diagnosis. Dynamic videos provide detailedthree-dimensional information about the lesion, while static images capture thetypical sections of the lesion. In this work, we propose a multi-modalitybreast tumor diagnosis model to imitate the diagnosing process of radiologists,which learns the features of both static images and dynamic video and exploresthe potential relationship between the two modalities. Considering that staticimages are carefully selected by professional radiologists, we propose toaggregate dynamic video features under the guidance of domain knowledge fromstatic images before fusing multi-modality features. Our work is validated on abreast ultrasound dataset composed of 897 sets of ultrasound images and videos.Experimental results show that our model boosts the performance ofBenign/Malignant classification, achieving 90.0% in AUC and 81.7% in accuracy.

Breast Lesion Diagnosis Using Static Images and Dynamic Video