  Efficient polyp segmentation in healthcare plays a critical role in enabling
early diagnosis of colorectal cancer. However, the segmentation of polyps
presents numerous challenges, including the intricate distribution of
backgrounds, variations in polyp sizes and shapes, and indistinct boundaries.
Defining the boundary between the foreground (i.e. polyp itself) and the
background (surrounding tissue) is difficult. To mitigate these challenges, we
propose Multi-Scale Edge-Guided Attention Network (MEGANet) tailored
specifically for polyp segmentation within colonoscopy images. This network
draws inspiration from the fusion of a classical edge detection technique with
an attention mechanism. By combining these techniques, MEGANet effectively
preserves high-frequency information, notably edges and boundaries, which tend
to erode as neural networks deepen. MEGANet is designed as an end-to-end
framework, encompassing three key modules: an encoder, which is responsible for
capturing and abstracting the features from the input image, a decoder, which
focuses on salient features, and the Edge-Guided Attention module (EGA) that
employs the Laplacian Operator to accentuate polyp boundaries. Extensive
experiments, both qualitative and quantitative, on five benchmark datasets,
demonstrate that our MEGANet outperforms other existing SOTA methods under six
evaluation metrics. Our code is available at
https://github.com/UARK-AICV/MEGANet.
