RUIFAF_Net: Radiofrequency and Ultrasound Image Feature Attention and Fusion Network for Improved Breast Cancer Segmentation

Ultrasound imaging (US) is one of the most commonly used techniques for detecting breast lesions. However, due to the inherent properties of low contrast, speckle noise, and blurred boundaries in B-mode ultrasound images, the performance of breast lesion segmentation was significantly limited. In contrast, original radiofrequency (RF) data contains more detailed information. To enhance the performance of breast lesion segmentation and compensate for the information missing in ultrasound images, this study proposed a radiofrequency and ultrasound image feature attention and fusion network (RUIFAF Net) to comprehensively fuse the complementary information from both modalities. Specifically, we proposed a Feature Attention Module (FAM) and a Multi-level Fusion (MF) method to obtain multi-scale feature information and integrate contextual semantic information, respectively. Experimental results on OASBUD dataset demonstrate that our proposed RUIFAF_Net achieved higher Dice (0.8264 vs. 0.6468-0.8184) and IoU (0.7161 vs. 0.5243-0.7025) compared with the other six advanced methods.