Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. Contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer’s proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models.

中文翻译：

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RSAFormer：一种具有区域自注意力变换器的息肉分割方法

结肠镜检查非常重视结肠癌的早期筛查和临床诊断。实现息肉的精细分割仍然是一项具有挑战性的任务。然而，由于正常组织和息肉之间缺乏清晰且高度相似的边界，现有的最先进模型的分割能力仍然有限。为了解决这个问题，我们提出了一个带有变压器编码器的区域自注意力增强网络（RSAFormer）来捕获更鲁棒的特征。与其他优秀方法不同，RSAFormer 独特地采用双解码器结构来生成各种特征图。与通常使用单个解码器的传统方法相比，它在特征提取方面提供了更大的灵活性和细节。 RSAFormer 还引入了区域自注意力增强模块（RSA）来获取更准确的特征信息并促进低级和高级特征之间更强的相互作用。该模块增强了不确定区域以提取更精确的边界信息，这些区域由区域上下文表示。在五个常见息肉数据集上进行了大量实验，以证明 RSAFormer 的熟练程度。它在 Kvasir 和 ETIS 上分别实现了 92.2% 和 83.5% 的平均 Dice，优于大多数最先进的模型。