Breast Lesion Diagnosis Using Static Images and Dynamic Video
Yunwen Huang Hongyu Hu Ying Zhu Yi Xu
Deep learning based Computer Aided Diagnosis (CAD) systems have been
developed to treat breast ultrasound. Most of them focus on a single ultrasound
imaging modality, either using representative static images or the dynamic
video of a real-time scan. In fact, these two image modalities are
complementary for lesion diagnosis. Dynamic videos provide detailed
three-dimensional information about the lesion, while static images capture the
typical sections of the lesion. In this work, we propose a multi-modality
breast tumor diagnosis model to imitate the diagnosing process of radiologists,
which learns the features of both static images and dynamic video and explores
the potential relationship between the two modalities. Considering that static
images are carefully selected by professional radiologists, we propose to
aggregate dynamic video features under the guidance of domain knowledge from
static images before fusing multi-modality features. Our work is validated on a
breast ultrasound dataset composed of 897 sets of ultrasound images and videos.
Experimental results show that our model boosts the performance of
Benign/Malignant classification, achieving 90.0% in AUC and 81.7% in accuracy.