Abstract
A novel deep hybrid Residual-SwinCA-Net segmentation framework is proposed in the study for addressing such challenges by extracting locally correlated and robust features, incorporating residual CNN modules. Furthermore, for learning global dependencies, Swin Transformer blocks are customized using internal residual pathways, which reinforce gradient stability, refine local patterns, and facilitate global feature fusion. Formerly, for enhancing tissue continuity, ultrasound noise suppressions, and accentuating fine structural transitions Laplacian-of-Gaussian regional operator is applied, and for maintaining the morphological integrity of malignant lesion contours, a boundary-oriented operator has been incorporated. Subsequently, a contraction strategy was applied stage-wise by progressively reducing features-map progressively for capturing scale invariance and enhancing the robustness of structural variability. In addition, each decoder level prior augmentation integrates a new Multi-Scale Channel Attention and Squeezing (MSCAS) module. The MSCAS selectively emphasizes encoder salient maps, retains discriminative global context, and complementary local structures with minimal computational cost while suppressing redundant activations. Finally, the Pixel-Attention module encodes class-relevant spatial cues by adaptively weighing malignant lesion pixels while suppressing background interference. The Residual-SwinCA-Net and existing CNNs/ViTs techniques have been implemented on the publicly available BUSI dataset. The proposed Residual-SwinCA-Net framework outperformed and achieved 99.29% mean accuracy, 98.74% IoU, and 0.9041 Dice for breast lesion segmentation. The proposed Residual-SwinCA-Net framework improves the BUSI lesion diagnostic performance and strengthens timely clinical decision-making.
Abstract (translated)
本研究提出了一种新颖的深度混合残差-SwinCA网络分割框架，旨在通过提取局部关联且鲁棒的特征，并结合残差CNN模块来应对相关挑战。此外，在学习全局依赖关系时，利用内部残差路径对Swin Transformer块进行了定制化处理，从而增强梯度稳定性、细化局部模式并促进全局特征融合。为了提升组织连续性、抑制超声噪声以及强调精细结构变化，采用了高斯拉普拉斯（Laplacian-of-Gaussian）区域算子。同时，为保持恶性病变轮廓的形态完整性，引入了一种边界导向操作器。随后，在每个阶段逐步减少特征图以捕获尺度不变性和增强结构性变异性的同时实施了一个收缩策略。此外，每一级解码器之前都集成了一个新的多尺度通道注意力及压缩（MSCAS）模块，该模块在最小化计算成本的情况下，选择性地强调编码器显著图、保留有区分力的全局上下文，并且补充局部结构，同时抑制冗余激活。 最后，像素注意模块通过自适应加权恶性病变像素来编码与类别相关的空间线索，从而抑制背景干扰。Residual-SwinCA-Net框架和现有的CNNs/ViTs技术已在公开可用的BUSI数据集上进行了实现。实验结果表明，提出的Residual-SwinCA-Net框架优于现有方法，并在乳腺病灶分割方面达到了99.29%的平均准确率、98.74%的IoU以及0.9041的Dice系数。该框架提高了BUSI病变诊断性能，从而增强了临床决策的有效性和及时性。
URL
https://arxiv.org/abs/2512.08243
