BI-Modal Ultrasound Breast Cancer Diagnosis Via Multi-View Deep Neural Network SVM

Abstract

B-mode ultrasound and ultrasound elastography are two routine diagnostic modalities for breast cancer. Unfortunately, few efforts have paid attention to learn bi-modal ultrasound jointly. By combining multi-view deep mapping-based feature representation with SVM-based classification, we proposed a novel integrated deep learning model, multi-view deep neural network support vector machine (MDNNSVM), to achieve breast cancer diagnosis on bi-modal ultrasound. In particular, multi-view representation learning extracts and fuses the various ultrasound characteristics (also including hardness information of soft tissue) effectively to differentiate benign breast lesions from malignant. Further, the SVM-based objective function is used to learn a classifier jointly with DNN to improve diagnostic accuracy significantly. The experimental results on a real-world dataset of breast cancer verify the effectiveness of the MDNNSVM with the best value of classification accuracy (86.36%) and AUC (0.9079).