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

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.

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.

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.

The proposed method provides a lightweight detection framework for breast ultrasound images by jointly modeling contextual and structural features.