融合注意力机制的肠道息肉分割多尺度卷积神经网络

肠道息肉分割能够提供息肉在结肠中的位置和形态信息，方便医生依据其结构变化程度来推断 癌变可能性，有利于结肠癌的早期诊断和治疗。针对许多现有的卷积神经网络所提取的多尺度特征有限，且常 引入冗余和干扰特征，难以应对复杂多变的肠道息肉分割问题，提出了一种融合注意力机制的肠道息肉分割多 尺度卷积神经网络(CNN)。首先，设计不同比例金字塔池化策略提取丰富的多尺度上下文信息；然后，通过在 网络中融入通道注意力机制，模型能够根据目标自适应地选择合适的局部上下文信息和全局上下文信息进行特 征集成；最后，联合金字塔池化策略和通道注意力机制构建多尺度有效语义融合解码网络，增强模型对形状、 大小复杂多变的肠道息肉分割的鲁棒性。实验结果表明，本文模型分割的 Dice 系数、IoU 和灵敏度在 CVC-ClinicDB 数据集上分别为 90.6%，84.4%和 91.1%，在 ETIS-Larib 数据集上分别为 80.6%，72.6%和 79.0%， 其能够从肠镜图像中准确、有效地分割出肠道息肉。

Multi-scale convolutional neural network incorporating attention mechanism for intestinal polyp segmentation

Intestinal polyp segmentation provides the location and morphology of polyps in colon, allowing doctors to infer the possibility of canceration according to the degree of structural deformation, which facilitates the early diagnosis and treatment of colon cancer. In view of the limited multi-scale features extracted by many existing convolutional neural networks (CNN), and the frequently caused redundant and interfering features, it is difficult to extract the complex and variable targets. To address this challenge, a multi-scale convolutional neural network incorporating attention mechanism was proposed for intestinal polyp segmentation. Specifically, the pyramid strategy based on different scales of pooling was designed to capture the rich multi-scale context information. Then a channel attention mechanism was incorporated into the network so that the model could adaptively select appropriate local and global contextual information for feature integration based on the region of interest. Following that, by combining the pyramid pooling strategy and the channel attention mechanism, a multi-scale effective semantic fusion decoder network was constructed to improve the model robustness for segmentation of intestinal polyps with complex and variable shapes and sizes. The experimental results show that the Dice coefficient, IoU, and sensitivity produced by the proposed model reach 90.6%, 84.4%, and 91.1% on the CVC-ClinicDB dataset, and 80.6%, 72.6%, and 79.0% on the ETIS-Larib dataset, indicating that the proposed model could accurately and effectively segments polyps in colonoscopy images.