Breast ultrasound imaging plays a crucial role in early breast cancer screening and diagnosis due to its noninvasive nature and cost-effectiveness. However, accurate lesion segmentation remains challenging because of severe speckle noise, low contrast, and blurred tumor boundaries. To address these issues, this paper proposes SEFF-Net, a novel edge-aware feature fusion network with a U-shaped encoder–decoder architecture to capture multi-level semantic representations for breast ultrasound image segmentation task. To enhance boundary perception, a Self-learning Edge Enhancement Module is embedded in the shallow encoding stages, while a Spatial Feature Fusion Module is introduced to effecti... More >