GFA-Net: Global Feature Aggregation Network Based on Contrastive Learning for Breast Lesion Automated Segmentation in Ultrasound Images

Abstract

Accurate automatic segmentation of breast lesions in ultrasound images is a challenging auxiliary diagnostic task for automated deployment in breast cancer screening. In this study, a contrastive learning-based global feature aggregation network (GFA-Net) is proposed to reduce false detections and missed detections, thereby providing computer vision assistance for the development of automated equipment for breast screening. The method first utilizes the feature extraction layer to extract multiscale feature maps from the ultrasound image and uses atrous spatial pyramid pooling (ASPP) to enhance the receptive field of the feature. In order to better utilize the spatial-channel complementary information between multiscale features, a global feature aggregation (GFA) module is proposed. This module can effectively utilize shallow features to extract deep features. Fine segmentation of tumor boundaries in ultrasound images is also crucial as it can reveal the edge diagnostic features of benign and malignant tumors. Therefore, a result fine repair (RFR) module is developed to refine the boundaries of segmented lesions. In addition, a contrastive deep supervision (CDS) method based on contrastive learning is designed, which can introduce the loss of contrastive learning into the process of deep supervision and use the correlation between different data in the same batch of training to improve the feature extraction ability of each extraction stage of the backbone network. The experimental results show that our GFA-Net has better segmentation performance than other existing advanced methods. The applicability analysis also indicates that our method still has good generalization ability for different medical ultrasound images and still maintains strong competitiveness compared to advanced methods.