DBNet: a dual-branch network for breast cancer classification in ultrasound images

Computer-aided diagnosis has been widely used in breast ultrasound images, and many deep learning-based models have emerged. However, the datasets used for breast ultrasound classification face the problem of category imbalance, which limits the accuracy of breast cancer classification. In this work, we propose a novel dual-branch network (DBNet) to alleviate the imbalance problem and improve classification accuracy. DBNet is constructed by conventional learning branch and re-balancing branch in parallel, which take universal sampling data and reversed sampling data as inputs, respectively. Both branches adopt ResNet-18 to extract features and share all the weights except for the last residual block. Additionally, both branches use the same classifier and share all the weights. The cross-entropy loss of each branch is calculated using the output logits and the corresponding groundtruth labels. The total loss of DBNet is designed as a linear weighted sum of two branches' losses. To evaluate the performance of the DBNet, we conduct breast cancer classification on the dataset composed of 6309 ultrasound images with malignant nodules and 3527 ultrasound images with benign nodules. Furthermore, ResNet-18 and bilateral-branch network (BBN) are utilized as baselines. The results demonstrate that DBNet yields a result of 0.854 in accuracy, which outperforms the ResNet-18 and the BBN by 2.7% and 1.1%, respectively.