A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer
Ziqi Yang, Xun Gong, Ying Guo, Wenbin Liu
Abstract
In clinical medicine, the contrast-enhanced ultrasound (CEUS) has been a commonly used imaging modality for diagnosis of breast tumor. However, most researchers in computer vision field only focus on B-mode ultrasound image which does not get good results. To improve the accuracy of classification, first, we propose a novel method, i.e., a Temporal Sequence Dual-Branch Network (TSDBN) which, for the first time, can use B-mode ultrasound data and CEUS data simultaneously. Second, we designed a new Gram matrix to model the temporal sequence, and then proposed a Temporal Sequence Regression Mechanism (TSRM), which is a novel method to extract the enhancement features from CEUS video based on the matrix. For B-mode ultrasound branch, we use the traditional ResNeXt network for feature extraction. While CEUS branch uses ResNeXt + R(2 + 1) D network as the backbone network. We propose a TSRM to learning temporal sequence relationship among frames, and design a Shuffle Temporal Sequence Mechanism (STSM) to shuffle temporal sequences, the purpose of which is to further enhance temporal information among frames. Experimental results show that the proposed TSRM could use temporal information effectively and the accuracy of TSDBN is higher than that of state-of-art approaches in breast cancer classification by nearly 4%.