# MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation

arXiv: https://arxiv.org/abs/2309.03329 · HTML: https://mlsanity.com/paper/2309.03329/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Reverse Attention for Salient Object Detection](https://mlsanity.com/paper/1807.09940/) (2018)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Lightweight Pyramid Networks for Image Deraining](https://mlsanity.com/paper/1805.06173/) (2018)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [Adaptive Context Selection for Polyp Segmentation](https://mlsanity.com/paper/2301.04799/) (2023)
- [Laplacian-Steered Neural Style Transfer](https://mlsanity.com/paper/1707.01253/) (2017)
- [Offset Curves Loss for Imbalanced Problem in Medical Segmentation](https://mlsanity.com/paper/2012.02463/) (2020)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data](https://mlsanity.com/paper/2201.05905/) (2022)
- [3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation](https://mlsanity.com/paper/2203.08965/) (2022)
- [DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation](https://mlsanity.com/paper/2112.13559/) (2021)
- [M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation](https://mlsanity.com/paper/2306.08600/) (2023)
- [Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation](https://mlsanity.com/paper/2301.06673/) (2023)

## Built on by (most cited first, 28 of 28)

- [Controllable-LPMoE: Adapting to Challenging Object Segmentation via Dynamic Local Priors from Mixture-of-Experts](https://mlsanity.com/paper/2510.21114/) (2025)
- [PRS-Med: Position Reasoning Segmentation in Medical Imaging](https://mlsanity.com/paper/2505.11872/) (2025)
- [SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation](https://mlsanity.com/paper/2412.08482/) (2024)
- [SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data](https://mlsanity.com/paper/2412.16078/) (2024)
- [Nested Unfolding Network for Real-World Concealed Object Segmentation](https://mlsanity.com/paper/2511.18164/) (2025)
- [Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion](https://mlsanity.com/paper/2602.01183/) (2026)
- [SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation](https://mlsanity.com/paper/2406.14819/) (2024)
- [From SAM to DINOv2: Towards Distilling Foundation Models to Lightweight Baselines for Generalized Polyp Segmentation](https://mlsanity.com/paper/2512.09307/) (2025)
- [A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos](https://mlsanity.com/paper/2403.11699/) (2024)
- [HiFiSeg: High-Frequency Information Enhanced Polyp Segmentation with Global-Local Vision Transformer](https://mlsanity.com/paper/2410.02528/) (2024)
- [Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation](https://mlsanity.com/paper/2507.01509/) (2025)
- [Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2507.17281/) (2025)
- [Small but Mighty: Dynamic Wavelet Expert-Guided Fine-Tuning of Large-Scale Models for Optical Remote Sensing Object Segmentation](https://mlsanity.com/paper/2601.09108/) (2026)
- [ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation](https://mlsanity.com/paper/2604.14755/) (2026)
- [KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation](https://mlsanity.com/paper/2312.08555/) (2023)
- [MFDS-Net: Multi-Scale Feature Depth-Supervised Network for Remote Sensing Change Detection with Global Semantic and Detail Information](https://mlsanity.com/paper/2405.01065/) (2024)
- [DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps](https://mlsanity.com/paper/2412.00888/) (2024)
- [MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection](https://mlsanity.com/paper/2507.02668/) (2025)
- [Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation](https://mlsanity.com/paper/2508.06517/) (2025)
- [MPCGNet: A Multiscale Feature Extraction and Progressive Feature Aggregation Network Using Coupling Gates for Polyp Segmentation](https://mlsanity.com/paper/2511.11032/) (2025)
- [Multi-Modal Monocular Endoscopic Depth and Pose Estimation with Edge-Guided Self-Supervision](https://mlsanity.com/paper/2602.17785/) (2026)
- [Polyp Segmentation Using Wavelet-Based Cross-Band Integration for Enhanced Boundary Representation](https://mlsanity.com/paper/2603.03682/) (2026)
- [Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation](https://mlsanity.com/paper/2603.17746/) (2026)
- [Sharpening Lightweight Models for Generalized Polyp Segmentation: A Boundary Guided Distillation from Foundation Models](https://mlsanity.com/paper/2604.17865/) (2026)
- [RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects](https://mlsanity.com/paper/2605.15450/) (2026)
- [Multi-view feature High-order Fusion for Space Weak Object Detection and Segmentation](https://mlsanity.com/paper/2606.15118/) (2026)
- [OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations](https://mlsanity.com/paper/2607.06007/) (2026)
- [Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation](https://mlsanity.com/paper/2607.17208/) (2026)

## Abstract (arXiv)

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.