论文标题
胃肠道内窥镜成像中伪影的深度学习架构的性能比较
Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging
论文作者
论文摘要
内窥镜图像通常包含几个工件。伪像显着影响图像分析导致计算机辅助诊断。卷积神经网络（CNN）是一种深度学习的类型，可以消除此类伪像。已经为CNN提出了各种体系结构，而伪影的准确性取决于体系结构的选择。因此，有必要根据所选的体系结构确定伪像的去除精度。在这项研究中，我们专注于内窥镜外科手术仪器作为工件，并使用七个不同的CNN架构确定并讨论伪影去除精度。
Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can removes such artifacts. Various architectures have been proposed for the CNNs, and the accuracy of artifact removal varies depending on the choice of architecture. Therefore, it is necessary to determine the artifact removal accuracy, depending on the selected architecture. In this study, we focus on endoscopic surgical instruments as artifacts, and determine and discuss the artifact removal accuracy using seven different CNN architectures.
