Channel Attention Module with Multi-scale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image

Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in their lifetime. Mortality due to breast cancer can be reduced by its early and precise diagnosis. Many studies have investigated methods for segmentation, and computer-aided diagnosis based on deep learning techniques, in particular, has recently gained attention. However, recently proposed methods such as fully convolutional network (FCN), SegNet, and U-Net still need to be further improved to provide better semantic segmentation when diagnosing breast cancer by ultrasound imaging, because of their low performance. In this article, we propose a channel attention module with multiscale grid average pooling (MSGRAP) for the precise segmentation of breast cancer regions in ultrasound images. We demonstrate the effectiveness of the channel attention module with MSGRAP for semantic segmentation and develop a novel semantic segmentation network with the proposed attention module for the precise segmentation of breast cancer regions in ultrasound images. While a conventional convolutional operation cannot use global spatial information on input images and only use the small local information in a kernel of a convolution filter, the proposed attention module allows using both global and local spatial information. In addition, through ablation studies, we come up with a network architecture for precise breast cancer segmentation in an ultrasound image. The proposed network was constructed with an open-source breast cancer ultrasound image data set, and its performance was compared with those of other state-of-the-art deep-learning models for the segmentation of breast cancer. The experimental results showed that our network outperformed other segmentation methods, and the proposed channel attention module improved the performance of the network for breast cancer segmentation in ultrasound images.