# MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection

arXiv: https://arxiv.org/abs/2507.02668 · HTML: https://mlsanity.com/paper/2507.02668/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction](https://mlsanity.com/paper/1610.09736/) (2016)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Lesion-aware Dynamic Kernel for Polyp Segmentation](https://mlsanity.com/paper/2301.04904/) (2023)
- [Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1805.08620/) (2018)
- [MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation](https://mlsanity.com/paper/2309.03329/) (2023)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation](https://mlsanity.com/paper/2005.14461/) (2020)
- [Polyper: Boundary Sensitive Polyp Segmentation](https://mlsanity.com/paper/2312.08735/) (2023)
- [Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey](https://mlsanity.com/paper/2401.11734/) (2024)
- [SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation](https://mlsanity.com/paper/2306.10773/) (2023)
- [Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation](https://mlsanity.com/paper/2405.11151/) (2024)
- [EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling](https://mlsanity.com/paper/2405.11846/) (2024)

## Abstract (arXiv)

Colorectal polyp segmentation is critical for early detection of colorectal cancer, yet weak and low contrast boundaries significantly limit automated accuracy. Existing deep models either blur fine edge details or rely on handcrafted filters that perform poorly under variable imaging conditions. We propose MEGANet-W, a Wavelet Driven Edge Guided Attention Network that injects directional, parameter free Haar wavelet edge maps into each decoder stage to recalibrate semantic features. The key novelties of MEGANet-W include a two-level Haar wavelet head for multi-orientation edge extraction; and Wavelet Edge Guided Attention (W-EGA) modules that fuse wavelet cues with boundary and input branches. On five public polyp datasets, MEGANet-W consistently outperforms existing methods, improving mIoU by up to 2.3% and mDice by 1.2%, while introducing no additional learnable parameters. This approach improves reliability in difficult cases and offers a robust solution for medical image segmentation tasks requiring precise boundary detection.