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.

翻译：结肠息肉的自动精准分割对于结直肠癌的早期诊断至关重要。先进的深度学习模型已在息肉分割中展现出令人瞩目的结果，但在多尺度特征表示与泛化能力方面仍存在局限性。为解决上述问题，本文提出RaBiT编码器-解码器模型，该模型在编码器中采用轻量级Transformer架构以建模多层级全局语义关系，解码器由若干具备反向注意力模块的双向特征金字塔层级构成，旨在优化不同层级特征图的融合并渐进式精炼息肉边界。我们还提出了反向注意力模块的轻量化方案，使其更适用于多类别分割任务。在多个基准数据集上的大量实验表明，本方法在所有数据集上均优于现有方法，同时保持较低的计算复杂度。此外，即便训练集与测试集具有不同特征，本方法在跨数据集实验中仍展现出强泛化能力。