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.
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 (MSVCNN) 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.
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