Accurate segmentation of skin cancer is crucial for doctors to identify and treat lesions. Researchers are increasingly using auxiliary modules with Transformers to optimize the model’s ability to process global context information and reduce detail loss. Additionally, diseased skin texture differsfrom normal skin, and pre-processed texture images can reflect the shape and edge information of the diseased area. We propose TMTrans (Texture Mixed Transformers). We have innovatively designed a dual axis attention mechanism (IEDA-Trans) that considers both global context and local information, as well as a multi-scale fusion (MSF) module that associates surface shape information with deep semantics. Additionally, we utilize TE(Texture Enhance) and SK(Skip connection) modules to bridge the semantic gap between encoders and decoders and enhance texture features. Our model was evaluated on multiple skin datasets, including ISIC 2016/2017/2018 and PH2, and outperformed other convolution and Transformer-based models. Furthermore, we conducted a generalization test on the 2018 DSB dataset, which resulted in a nearly 2% improvement in the Dice index, demonstrating the effectiveness of our proposed model.

中文翻译：

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TMTrans：用于医学图像分割的纹理混合变压器

皮肤癌的准确分割对于医生识别和治疗病变至关重要。研究人员越来越多地使用 Transformer 的辅助模块来优化模型处理全局上下文信息并减少细节丢失的能力。另外，病变皮肤纹理与正常皮肤不同，预处理的纹理图像可以反映病变区域的形状和边缘信息。我们提出 TMTrans（纹理混合变压器）。我们创新性地设计了一种同时考虑全局上下文和局部信息的双轴注意力机制（IEDA-Trans），以及将表面形状信息与深层语义关联起来的多尺度融合（MSF）模块。此外，我们利用 TE（纹理增强）和 SK（跳过连接）模块来弥合编码器和解码器之间的语义差距并增强纹理特征。我们的模型在多个皮肤数据集（包括 ISIC 2016/2017/2018 和 PH2）上进行了评估，并且优于其他基于卷积和 Transformer 的模型。此外，我们对 2018 DSB 数据集进行了泛化测试，Dice 指数提高了近 2%，证明了我们提出的模型的有效性。