论文标题
使用卷积神经网络自动分割
Automatic Polyp Segmentation Using Convolutional Neural Networks
论文作者
论文摘要
大肠癌是肺癌和全球乳腺癌后第三大常见的癌症死亡。结肠镜检查期间息肉的早期诊断可以降低结直肠癌的风险。计算机辅助诊断系统有可能应用于息肉筛查并减少缺失的息肉数量。在本文中，我们将不同深度学习体系结构的性能作为特征提取器，即Resnet，Densenet，InceptionV3，InceptionResnetv2和Se-Resnext在U-NET体系结构的编码部分中。我们验证了CVC-Clinic（Giana 2018）数据集上提出的集合模型的性能。 densenet169与U-NET体系结构结合的特征提取器优于其他对应物，精度为99.15 \％，骰子相似性系数为90.87％，Jaccard指数为83.82％。
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy. Computer-aided diagnosis systems have the potential to be applied for polyp screening and reduce the number of missing polyps. In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture. We validated the performance of presented ensemble models on the CVC-Clinic (GIANA 2018) dataset. The DenseNet169 feature extractor combined with U-Net architecture outperformed the other counterparts and achieved an accuracy of 99.15\%, Dice similarity coefficient of 90.87%, and Jaccard index of 83.82%.
