标题： 基于Transformer-CNN融合的深层皮肤病变分割：迈向智能皮肤癌分析 显示英文标题

标题： Deep Skin Lesion Segmentation with Transformer-CNN Fusion: Toward Intelligent Skin Cancer Analysis

摘要： 本文提出了一种基于改进的TransUNet架构的高精度语义分割方法，以解决皮肤病变图像中复杂病变结构、模糊边界和显著尺度变化的挑战。 该方法将Transformer模块集成到传统的编码器-解码器框架中，以建模全局语义信息，同时保留卷积分支以保持局部纹理和边缘特征。 这增强了模型对细粒度结构的感知能力。 还设计了边界引导的注意力机制和多尺度上采样路径，以提高病变边界定位和分割一致性。 为了验证该方法的有效性，进行了一系列实验，包括比较研究、超参数敏感性分析、数据增强效果、输入分辨率变化和训练数据划分比例测试。 实验结果表明，所提出的模型在mIoU、mDice和mAcc方面优于现有的代表性方法，展示了更强的病变识别准确性和鲁棒性。 特别是，在复杂场景中，该模型在边界重建和结构恢复方面表现更好，使其非常适合皮肤病变分析中自动化分割任务的关键需求。 显示英文摘要

摘要： This paper proposes a high-precision semantic segmentation method based on an improved TransUNet architecture to address the challenges of complex lesion structures, blurred boundaries, and significant scale variations in skin lesion images. The method integrates a transformer module into the traditional encoder-decoder framework to model global semantic information, while retaining a convolutional branch to preserve local texture and edge features. This enhances the model's ability to perceive fine-grained structures. A boundary-guided attention mechanism and multi-scale upsampling path are also designed to improve lesion boundary localization and segmentation consistency. To verify the effectiveness of the approach, a series of experiments were conducted, including comparative studies, hyperparameter sensitivity analysis, data augmentation effects, input resolution variation, and training data split ratio tests. Experimental results show that the proposed model outperforms existing representative methods in mIoU, mDice, and mAcc, demonstrating stronger lesion recognition accuracy and robustness. In particular, the model achieves better boundary reconstruction and structural recovery in complex scenarios, making it well-suited for the key demands of automated segmentation tasks in skin lesion analysis.