Scholarlink

<p>Breast cancer is a major health threat to women, and early detection via ultrasound imaging is vital for improving survival rates. However, ultrasound images often suffer from low contrast, blurry boundaries, and significant shape variations of tumors. To address these challenges, we propose the Semantic-aware Breast Tumors Segmentation Network (SBTSN). The core contribution is a novel Semantic-aware Block (SAB) designed to refine tumor boundaries by leveraging deep-level semantic guidance to calibrate low-level spatial features. Unlike traditional static attention mechanisms, the SAB dynamically generates attention maps from decoder feedback to suppress background noise and enhance target-related features. Integrated into a lightweight U-Net-based backbone, SBTSN achieves a superior balance between segmentation accuracy and computational efficiency. Extensive experiments on the BUSI and STU datasets demonstrate that SBTSN outperforms state-of-the-art models, achieving superior scores in key segmentation metrics such as Dice and IoU, effectively reducing missed detections in complex clinical scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Semantic-aware breast tumors segmentation network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

查看全文
  • Liang Chen,
  • Ting Hu,
  • Donghua Yu,
  • Xinsheng Lai,
  • Zhe Yuan,
  • Huxiong Li,
  • Chunping Tan

摘要

Breast cancer is a major health threat to women, and early detection via ultrasound imaging is vital for improving survival rates. However, ultrasound images often suffer from low contrast, blurry boundaries, and significant shape variations of tumors. To address these challenges, we propose the Semantic-aware Breast Tumors Segmentation Network (SBTSN). The core contribution is a novel Semantic-aware Block (SAB) designed to refine tumor boundaries by leveraging deep-level semantic guidance to calibrate low-level spatial features. Unlike traditional static attention mechanisms, the SAB dynamically generates attention maps from decoder feedback to suppress background noise and enhance target-related features. Integrated into a lightweight U-Net-based backbone, SBTSN achieves a superior balance between segmentation accuracy and computational efficiency. Extensive experiments on the BUSI and STU datasets demonstrate that SBTSN outperforms state-of-the-art models, achieving superior scores in key segmentation metrics such as Dice and IoU, effectively reducing missed detections in complex clinical scenarios.