论文标题
自动视频内窥镜数据分析的两流深功能建模
Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
论文作者
论文摘要
自动化内窥镜检查过程中捕获的胃肠道（GI）图像的分析对患者具有巨大的潜在益处，因为它可以为医生提供诊断支持，并通过人为错误减少错误。为了进一步发展此类方法，我们提出了一个两流模型，用于内窥镜图像分析。我们的模型通过通过新颖的关系网络模型映射其固有关系，以更好地建模症状并对图像进行分类，从而融合了深度特征输入的两个流。与基于手工功能的模型相反，我们提出的网络能够自动学习功能，并且在两个公共数据集上的现有最新方法胜过：Kvasir和Nerthus。我们的广泛评估说明了拥有两个输入流而不是单个流的重要性，并且还展示了提出的关系网络体系结构的优点以结合这些流。
Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error. To further the development of such methods, we propose a two-stream model for endoscopic image analysis. Our model fuses two streams of deep feature inputs by mapping their inherent relations through a novel relational network model, to better model symptoms and classify the image. In contrast to handcrafted feature-based models, our proposed network is able to learn features automatically and outperforms existing state-of-the-art methods on two public datasets: KVASIR and Nerthus. Our extensive evaluations illustrate the importance of having two streams of inputs instead of a single stream and also demonstrates the merits of the proposed relational network architecture to combine those streams.
