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

Authors

Xiangqiong Wu, Yujie Tang, Yaxuan Zhou, Peng Wang

Published in

Frontiers in oncology. Volume 16. Pages 1857024. Epub Jun 26, 2026.

Abstract

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.

PMID:
42434751
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.