Hajabdollahi Mohsen, Esfandiarpoor Reza, Najarian Kayvan, Karimi Nader, Samavi Shadrokh, Reza Soroushmehr S M
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:7227-7230. doi: 10.1109/EMBC.2019.8857751.
Wireless capsule endoscopy (WCE) is a swallowable device used for screening different parts of the human digestive system. Automatic WCE image analysis methods reduce the duration of the screening procedure and alleviate the burden of manual screening by medical experts. Recent studies widely employ convolutional neural networks (CNNs) for automatic analysis of WCE images; however, these studies do not consider CNN's structural and computational complexities. In this paper, we address the problem of simplifying the CNN's structure. A low complexity CNN structure for bleeding zone detection is proposed which takes a single patch as input and then outputs a segmented patch of the same size. The proposed network is inspired by the FCN paradigm with a simplified structure. Since it is based on image patches, the resulting network benefits from moderate-sized intermediate feature maps. Moreover, the problem of redundant computations in patch-based methods is circumvented by non-overlapping patch processing. The proposed method is evaluated using the publicly available KID dataset for WCE image analysis. Experimental results show that the proposed network has better accuracy and AUC than previous structures while requiring less computational operations.
无线胶囊内镜（WCE）是一种可吞咽的设备，用于筛查人体消化系统的不同部位。自动WCE图像分析方法可缩短筛查过程的持续时间，并减轻医学专家手动筛查的负担。最近的研究广泛采用卷积神经网络（CNN）对WCE图像进行自动分析；然而，这些研究没有考虑CNN的结构和计算复杂性。在本文中，我们解决了简化CNN结构的问题。提出了一种用于出血区域检测的低复杂度CNN结构，该结构以单个图像块作为输入，然后输出相同大小的分割图像块。所提出的网络受到具有简化结构的全卷积网络（FCN）范式的启发。由于它基于图像块，因此生成的网络受益于中等大小的中间特征图。此外，通过非重叠图像块处理避免了基于图像块的方法中的冗余计算问题。使用公开可用的用于WCE图像分析的KID数据集对所提出的方法进行评估。实验结果表明，所提出的网络比以前的结构具有更高的准确率和曲线下面积（AUC），同时所需的计算操作更少。
