ETRT-Net: Efficient Non-Local Transformer and Residual Triplet Attention for Breast Lesion Segmentation

Breast cancer stands as one of the leading causes of mortality among women. Accurate lesion segmentation is crucial for timely clinical intervention. However, the varying tumor morphologies and unclear boundaries present in breast ultrasound images pose significant challenges for existing methods, often hindering their ability to produce satisfactory results. This study proposes an Efficient Non-Local Transformer and Residual Triple Attention Network (ETRT-Net). The network combines three modules: the Efficient Non-Local Transformer Block (ENLTB), the Residual Triple Attention Block (RTAB), and the Residual Triple Conv Attention Block (RTCAB). The ENLTB module extends the receptive field of the model with a time complexity of O(N), which aids in accurately identifying breast lesion boundaries. Additionally, we innovatively propose the RTAB and RTCAB modules, which employ a triple attention mechanism to capture cross-dimensional feature dependencies across three parallel branches, enhancing the model's sensitivity to feature correlations. Through experiments on two open-access breast ultrasound datasets, our method provides more accurate and reliable segmentation, highlighting its potential for improving diagnostic capabilities in clinical settings.