标题： MEGANet-W：一种用于弱边界息肉检测的波浪驱动边缘引导注意力框架 显示英文标题

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

摘要： 结直肠息肉分割对于早期检测结直肠癌至关重要，但弱且对比度低的边界显著限制了自动化的准确性。 现有的深度模型要么模糊了细小的边缘细节，要么依赖于在不同成像条件下表现不佳的手工设计滤波器。 我们提出了MEGANet-W，一种基于小波的边缘引导注意网络，它将方向性、无需参数的哈尔小波边缘图注入每个解码器阶段以重新校准语义特征。 我们的两个主要贡献是：(1) 一个两级哈尔小波头用于多方向边缘提取；以及(2) 小波边缘引导注意（WEGA）模块，这些模块将小波线索与反向和输入分支融合。 在五个公开的息肉数据集上，MEGANetW始终优于现有方法，mIoU提高了高达2.3%，mDice提高了1.2%，同时没有引入额外的可学习参数。 显示英文摘要

摘要： 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. Our two main contributions are: (1) a two-level Haar wavelet head for multi orientation edge extraction; and (2) Wavelet Edge Guided Attention (WEGA) modules that fuse wavelet cues with reverse and input branches. On five public polyp datasets, MEGANetW consistently outperforms existing methods, improving mIoU by up to 2.3% and mDice by 1.2%, while introducing no additional learnable parameters.