Abstract
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 EGANet outperforms other existing SOTA methods under six evaluation metrics. Our code is available at \url{this https URL}
Abstract (translated)
在医疗保健中,有效的 polyp 分割在早期诊断 colorectal cancer 中发挥着关键作用。然而, polyp 分割面临着许多挑战,包括背景的精细分布、 polyp 大小和形状的变化以及模糊的边界。定义前景(即 polyp 本身)和背景(周围组织)之间的边界并不容易。为了缓解这些挑战,我们提出了 Multi-Scale Edge-Guided Attention Network (MEGANet) 专门为 colonoscopies 图像中的 polyp 分割而设计。该网络从经典的边缘检测技术和注意力机制的融合中汲取灵感。通过结合这些技术,MegaNet 有效地保留了高频信息,特别是边缘和边界,这些边缘通常会随着神经网络的深度而磨损。MegaNet 设计为一个完整的端到端框架,包括三个关键模块:编码器,负责从输入图像中提取和抽象特征;解码器,专注于引人注目的特征;以及 Edge-Guided Attention module (EGA),采用高斯函数操作来加强 polyp 边界。在五个基准数据集上进行了大量的定性和定量实验,证明了我们的 EGANet 在六个评估指标上优于其他现有 SOTA 方法。我们的代码可供参考于 \url{this https URL}。
URL
https://arxiv.org/abs/2309.03329
