  Precise polyp segmentation is vital for the early diagnosis and prevention of
colorectal cancer (CRC) in clinical practice. However, due to scale variation
and blurry polyp boundaries, it is still a challenging task to achieve
satisfactory segmentation performance with different scales and shapes. In this
study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for
polyp segmentation, which can fully make use of cross-level and multi-scale
features to enhance the performance of polyp segmentation. Specifically, we
first present an Edge-aware Guidance Module (EGM) to combine the low-level
features with the high-level features to learn an edge-enhanced feature, which
is incorporated into each decoder unit using a layer-by-layer strategy.
Besides, a Scale-aware Convolution Module (SCM) is proposed to learn
scale-aware features by using dilated convolutions with different ratios, in
order to effectively deal with scale variation. Further, a Cross-level Fusion
Module (CFM) is proposed to effectively integrate the cross-level features,
which can exploit the local and global contextual information. Finally, the
outputs of CFMs are adaptively weighted by using the learned edge-aware
feature, which are then used to produce multiple side-out segmentation maps.
Experimental results on five widely adopted colonoscopy datasets show that our
EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of
generalization and effectiveness.
