BAMT-Net: a boundary-aware multi-task framework for 3D breast ultrasound lesion segmentation and classification.

This study introduces an efficient Automated Breast Volume Scanner (ABVS) image analysis framework designed to address the limitations of traditional screening methods-namely low diagnostic accuracy, excessive reliance on operator expertise, and the increased workload associated with manual interpretation of three-dimensional imaging. Our primary objective was to develop a single, end-to-end network that simultaneously performs lesion segmentation and benign-malignant classification using a multi-task learning strategy. The proposed model integrates several innovations. A boundary-aware auxiliary task and a semantic guidance module were designed to enhance the correlation between accurate boundary delineation and classification. We also developed a hybrid weighted attention mechanism that combines spatial and channel-wise information, improving the model’s ability to identify lesions against complex tissue backgrounds. Our findings show that this integrated approach significantly improves performance in both segmentation and classification. By leveraging the intrinsic relationship between the two tasks, the model achieves more precise lesion capture and feature extraction. This framework offers a practical and effective automated solution for clinical breast lesion assessment, enhancing diagnostic accuracy and efficiency while reducing the interpretive burden on radiologists.