A synergistic framework integrating global context and structural features for breast ultrasound lesion detection.

Background: Accurate breast lesion detection in ultrasound images remains challenging due to speckle noise, acoustic artifacts, low contrast, and blurred lesion boundaries. Although YOLO-based detectors are efficient, they may not fully capture long-range contextual dependencies and directional structural information that are important for reliable lesion localization. Methods: We proposed a lightweight context-structure synergistic framework based on YOLOv13. A Dual-Stream Mamba Aggregation (DSMA) module is introduced to enhance contextual feature aggregation with linear-complexity state-space modeling, while a Structure-aware Axial Attention (SAA) module is used to model horizontal and vertical structural dependencies. The two modules are integrated in a stage-specific manner to improve feature representation with limited computational overhead. Results: On the BUV and WH-BUS datasets, the proposed method achieved competitive detection performance while maintaining 2.50M parameters, 6.4 GFLOPs, and 161.29 FPS. Ablation, cross-dataset, robustness, and visualization analysis showed that DSMA and SAA provide complementary benefits for contextual representation and structure-aware localization. Conclusion: The proposed method provides a lightweight detection framework for breast ultrasound images by jointly modeling contextual and structural features.