To overcome the difficulty of accurate polyp segmentation, a novel encoder–decoder network DFETC-Net is proposed, in which two encoders based on Swin Transformer and CNN are utilized to extract the global and local features respectively. Further, a new self-attention and convolution feature fusion module is designed to fuse the two branch features to enhance the feature representative capability and alleviate the influence of the semantic gap. In the bottleneck, a new multi-feature pyramid pooling module fuses all deep features from two branches to obtain multi-scale information and promote segmentation accuracy. The coordinate attention is used in the skip connections between two shallow CNN blocks and corresponding decoder blocks to pay more attention to doubtful and complicated regions. Extensive experiments demonstrate the proposed network outperforms several state-of-the-art methods in terms of both qualitative effects and quantitative measurements. All codes are available on https://github.com/LYJieH/DFETC-NET.

中文翻译：

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双分支特征提取网络结合Transformer和CNN进行息肉分割

为了克服息肉精确分割的困难，提出了一种新颖的编码器-解码器网络DFETC-Net，其中利用基于Swin Transformer和CNN的两个编码器分别提取全局和局部特征。进一步，设计了一种新的自注意力和卷积特征融合模块来融合两个分支特征，以增强特征表示能力并减轻语义差距的影响。在瓶颈中，新的多特征金字塔池化模块融合了两个分支的所有深层特征，以获得多尺度信息并提高分割精度。坐标注意力用于两个浅层 CNN 块和相应解码器块之间的跳跃连接，以更多地关注可疑和复杂的区域。大量的实验表明，所提出的网络在定性效果和定量测量方面都优于几种最先进的方法。所有代码均可在 https://github.com/LYJieH/DFETC-NET 上获取。