Hybrid CNN-Transformer models are designed to combine the advantages of Convolutional Neural Networks (CNNs) and Transformers to efficiently model both local information and long-range dependencies. However, most research tends to focus on integrating the spatial features of CNNs and Transformers, while overlooking the critical importance of channel features. This is particularly significant for model performance in low-quality medical image segmentation. Effective channel feature extraction can significantly enhance the model's ability to capture contextual information and improve its representation capabilities. To address this issue, we propose a hybrid CNN-Transformer model, CFFormer, and introduce two modules: the Cross Feature Channel Attention (CFCA) module and the X-Spatial Feature Fusion (XFF) module. The model incorporates dual encoders, with the CNN encoder focusing on capturing local features and the Transformer encoder modeling global features. The CFCA module filters and facilitates interactions between the channel features from the two encoders, while the XFF module effectively reduces the significant semantic information differences in spatial features, enabling a smooth and cohesive spatial feature fusion. We evaluate our model across eight datasets covering five modalities to test its generalization capability. Experimental results demonstrate that our model outperforms current state-of-the-art (SOTA) methods, with particularly superior performance on datasets characterized by blurry boundaries and low contrast.

本研究解决了当前医学图像分割中对通道特征重视不足的问题，通过提出新的混合CNN-Transformer模型CFFormer来改进模型性能。该模型引入了交叉特征通道注意力模块和X-空间特征融合模块，有效提高了对低质量医学图像中上下文信息的捕捉能力。实验结果表明，CFFormer在处理模糊边界和低对比度数据集上表现优于现有最先进的方法。

CFFormer：跨CNN-Transformer通道注意力与空间特征融合以改善低质量医学图像分割