Colonoscopy is a procedure to detect colorectal polyps which are the primary cause for developing colorectal cancer. However, polyp segmentation is a challenging task due to the diverse shape, size, color, and texture of polyps, shuttle difference between polyp and its background, as well as low contrast of the colonoscopic images. To address these challenges, we propose a feature enhancement network for accurate polyp segmentation in colonoscopy images. Specifically, the proposed network enhances the semantic information using the novel Semantic Feature Enhance Module (SFEM). Furthermore, instead of directly adding encoder features to the respective decoder layer, we introduce an Adaptive Global Context Module (AGCM), which focuses only on the encoder's significant and hard fine-grained features. The integration of these two modules improves the quality of features layer by layer, which in turn enhances the final feature representation. The proposed approach is evaluated on five colonoscopy datasets and demonstrates superior performance compared to other state-of-the-art models.

翻译：Colonoscopy 是检测作为培养直肠癌主要原因的直肠切片程序。 然而,由于聚虫的形状、大小、颜色和质地各不相同,聚虫及其背景之间的穿梭差异,以及结肠镜图象的低差异,聚谱分解是一项具有挑战性的任务。为了应对这些挑战,我们建议为结肠镜图像中准确的聚分化建立一个地物增强网络。具体地说,拟议的网络利用小说“语义特征增强模块(SFEM)”来增强语义信息。此外,我们引入了一个适应性全球背景模块(AGCM),该模块仅侧重于编码器的显著和硬细细微的特性。这两个模块的整合提高了地物层质量,这反过来又增强了最后的特征代表。 拟议的方法在五个结肠镜分析数据集上进行了评估,并展示了与其他状态模型相比的优异性。