Automatic and accurate segmentation of colon polyps is essential for early diagnosis of colorectal cancer. Advanced deep learning models have shown promising results in polyp segmentation. However, they still have limitations in representing multi-scale features and generalization capability. To address these issues, this paper introduces RaBiT, an encoder-decoder model that incorporates a lightweight Transformer-based architecture in the encoder to model multiple-level global semantic relationships. The decoder consists of several bidirectional feature pyramid layers with reverse attention modules to better fuse feature maps at various levels and incrementally refine polyp boundaries. We also propose ideas to lighten the reverse attention module and make it more suitable for multi-class segmentation. Extensive experiments on several benchmark datasets show that our method outperforms existing methods across all datasets while maintaining low computational complexity. Moreover, our method demonstrates high generalization capability in cross-dataset experiments, even when the training and test sets have different characteristics.

本文介绍了一种名为 RaBiT 的深度学习模型，该模型不仅融合了轻量级 Transformer 架构以建模多层全局语义关系，同时还使用了多个双向特征金字塔和反向注意模块来更好地融合各个级别的特征图并逐步细化息肉边界，实验证明该方法在多个基准数据集上优于现有方法，在保持低计算复杂度的同时具有较高的泛化能力，尤其是在训练集和测试集具有不同特征时表现更加突出。

RaBiT：一种使用双向特征金字塔网络和反向注意力的高效Transformer用于结直肠息肉分割