Multi-scale View-based Convolutional Neural Network for Breast Cancer Classification in Ultrasound Images

Breast cancer is the second leading cause of cancer-related death in women. Ultrasound imaging has been widely used for the early detection of breast cancer because of its superior ability in imaging dense breast tissue and its lack of ionizing radiation However, ultrasound imaging heavily depends on practitioners' experience and thus becomes relatively subjective. In this work, we proposed a novel multi-scale view-based convolutional neural network (MSV-CNN) to assist doctors to diagnose and improve classification accuracy. MSV-CNN takes full images, regions of interest (ROI), and the tumor regions with two times size of the ROI as input. It adopts three complementary branches to learn multi-scale view features from different views. The sub-networks in all branches have the same structure but with different parameters. The output of three branches is finally concatenated and fused by a fully connected layer for automated nodule classification. To assess the performance of our proposed network, we implemented breast ultrasound classification on the dataset containing 1560 images with benign nodules and 5367 images with malignant nodules. Furthermore, ResNet-18 models trained with different views were utilized as baselines. Experimental results showed that MSV-CNN achieved an average classification accuracy of 0.907. This preliminary study demonstrated that our proposed method is effective in the discrimination of breast nodules.