摘要翻译：
结直肠癌是美国常见的癌症之一。息肉是结肠癌的主要原因之一，早期发现息肉将增加癌症治疗的机会。本文提出了一种新的基于二值化权值的卷积神经网络的信息帧分类方法。提出的CNN是用结肠镜框架和框架的标签作为输入数据来训练的。我们还使用了二值化的权值和核函数来减小CNN的大小，使其适合于在医疗硬件中实现。我们使用Asu Mayo测试临床数据库评估我们提出的方法，该数据库包含不同患者的结肠镜视频。该方法的dice评分达到71.20%，准确率达到90%以上。
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英文标题：
《Classification of Informative Frames in Colonoscopy Videos Using
  Convolutional Neural Networks with Binarized Weights》
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作者：
Mojtaba Akbari, Majid Mohrekesh, Shima Rafiei, S.M. Reza Soroushmehr,
  Nader Karimi, Shadrokh Samavi, Kayvan Najarian
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Colorectal cancer is one of the common cancers in the United States. Polyp is one of the main causes of the colonic cancer and early detection of polyps will increase chance of cancer treatments. In this paper, we propose a novel classification of informative frames based on a convolutional neural network with binarized weights. The proposed CNN is trained with colonoscopy frames along with the labels of the frames as input data. We also used binarized weights and kernels to reduce the size of CNN and make it suitable for implementation in medical hardware. We evaluate our proposed method using Asu Mayo Test clinic database, which contains colonoscopy videos of different patients. Our proposed method reaches a dice score of 71.20% and accuracy of more than 90% using the mentioned dataset.
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PDF链接：
https://arxiv.org/pdf/1802.01387