标题： 用于超声图像乳腺肿瘤精确分割的混合注意力网络 显示英文标题

标题： Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images

摘要： 乳腺超声成像是一种有价值的早期乳腺癌检测工具，但由于固有的噪声、病灶尺度的变化以及模糊的边界，自动肿瘤分割具有挑战性。 为了解决这些挑战，我们提出了一种新颖的基于混合注意力的网络用于病灶分割。 我们提出的架构在编码器部分集成了预训练的DenseNet121，用于鲁棒特征提取，并且在解码器部分采用了多分支注意力增强结构，专门针对乳腺超声图像设计。 瓶颈模块结合了全局空间注意力（GSA）、位置编码（PE）和缩放点积注意力（SDPA），以学习全局上下文、空间关系和相对位置特征。 空间特征增强块（SFEB）嵌入到跳跃连接中，以细化和增强空间特征，使网络能够更有效地关注肿瘤区域。 一种结合二元交叉熵（BCE）和Jaccard指数损失的混合损失函数优化了像素级准确性和区域级重叠度量，增强了对类别不平衡和不规则肿瘤形状的鲁棒性。 在公开数据集上的实验表明，我们的方法优于现有方法，展示了其协助放射科医生进行早期和准确乳腺癌诊断的潜力。 显示英文摘要

摘要： Breast ultrasound imaging is a valuable tool for early breast cancer detection, but automated tumor segmentation is challenging due to inherent noise, variations in scale of lesions, and fuzzy boundaries. To address these challenges, we propose a novel hybrid attention-based network for lesion segmentation. Our proposed architecture integrates a pre-trained DenseNet121 in the encoder part for robust feature extraction with a multi-branch attention-enhanced decoder tailored for breast ultrasound images. The bottleneck incorporates Global Spatial Attention (GSA), Position Encoding (PE), and Scaled Dot-Product Attention (SDPA) to learn global context, spatial relationships, and relative positional features. The Spatial Feature Enhancement Block (SFEB) is embedded at skip connections to refine and enhance spatial features, enabling the network to focus more effectively on tumor regions. A hybrid loss function combining Binary Cross-Entropy (BCE) and Jaccard Index loss optimizes both pixel-level accuracy and region-level overlap metrics, enhancing robustness to class imbalance and irregular tumor shapes. Experiments on public datasets demonstrate that our method outperforms existing approaches, highlighting its potential to assist radiologists in early and accurate breast cancer diagnosis.