  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.
