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Accurate segmentation of polyps is a crucial step in the efficient diagnosis of colorectal cancer during screening procedures. The prevalent UNet-like encoder-decoder frameworks are commonly employed, due to their capability of capturing multi-scale contextual information efficiently. However, two major limitations hinder the network from achieving effective feature propagation and aggregation. Firstly, the skip connection only transmits a single scale feature to the decoder, which can result in limited feature representation. Secondly, the features are transmitted without any information filter, which is inefficient for performing feature fusion at the decoder. To address these limitations, we propose a novel feature enhancement network that leverages feature propagation enhancement and feature aggregation enhancement modules for more efficient feature fusion and multi-scale feature propagation. Specifically, the …