Polyp segmentation is a key aspect of colorectal cancer prevention, enabling early detection and guiding subsequent treatments. Intelligent diagnostic tools, including deep learning solutions, are widely explored to streamline and potentially automate this process. However, even with many powerful network architectures, there still comes the problem of producing accurate edge segmentation. In this paper, we introduce a novel network, namely RTA-Former, that employs a transformer model as the encoder backbone and innovatively adapts Reverse Attention (RA) with a transformer stage in the decoder for enhanced edge segmentation. The results of the experiments illustrate that RTA-Former achieves state-of-the-art (SOTA) performance in five polyp segmentation datasets. The strong capability of RTA-Former holds promise in improving the accuracy of Transformer-based polyp segmentation, potentially leading to better clinical decisions and patient outcomes. Our code will be publicly available on GitHub.

翻译：息肉分割是结直肠癌预防的关键环节，能够实现早期检测并指导后续治疗。包括深度学习解决方案在内的智能诊断工具已被广泛探索，以精简乃至自动化这一过程。然而，即使拥有众多强大的网络架构，仍存在难以实现精确边缘分割的问题。本文提出了一种名为RTA-Former的新型网络，该网络采用Transformer模型作为编码器主干，并在解码器中创新性地将反向注意力（Reverse Attention, RA）与Transformer阶段相结合，以增强边缘分割性能。实验结果表明，RTA-Former在五个息肉分割数据集上达到了最先进（SOTA）水平。RTA-Former的强大能力有望提升基于Transformer的息肉分割的准确性，从而改善临床决策与患者预后。我们的代码将公开在GitHub上。