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浙江大学学报(理学版)  2017, Vol. 44 Issue (4): 379-384    DOI: 10.3785/j.issn.1008-9497.2017.04.001
综述     
应用于平扫CT图像肺结节检测的深度学习方法综述
胡伟俭1, 陈为2, 冯浩哲2, 张天平2, 朱正茂2, 潘巧明1
1. 丽水学院 工学院, 浙江 丽水 323000;
2. 浙江大学 计算机学院 CAD&CG国家重点实验室, 浙江 杭州 310058
A survey of depth learning methods for detecting lung nodules by CT images
HU Weijian1, CHEN Wei2, FENG Haozhe2, ZHANG Tianping2, ZHU Zhengmao2, PAN Qiaoming1
1. Engineering College, Lishui University, Lishui 323000, Zhejiang Province, China;
2. State Key Lab of CAD & CG, College of Computer Science, Zhejiang University, Hangzhou 310058, China
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摘要: 肺癌是一种致死率很高的癌症.通过肺部平扫CT影像检测肺结节对肺癌早期诊断、治疗意义重大.全面介绍了一种革命性的图像识别技术——深度学习方法,在肺结节检测中的应用.首先,横向对比了不同卷积神经网络的结构及其在图像识别上的效果,其次着重分析了不同深度学习方法在训练肺结节分类器上的应用,包括faster-RCNN、迁移学习、残差学习以及迁移学习.还介绍了一些可用的肺部CT影像数据集供读者参考.
关键词: 深度学习肺结节卷积神经网络数据库    
Abstract: Lung cancer is one of the most aggressive cancers and detecting lung nodule by CT images at the early stage is of vital importance to treating lung cancer. This paper overviews the application of a revolutionary image recognition method, deep learning, in the detection of lung nodule. First, we contrast different convolutional neural network (CNN) architectures and their performance in image recognition. Then, we mainly focus on various deep learning methods including faster-RCNN, transfer learning, residual network and curriculum learning to train the classifier. We also introduce some available databases of lung CT images in the last section of our paper.
Key words: deep learning    lung nodule    CNN    database
收稿日期: 2017-05-23 出版日期: 2017-12-09
CLC:  TP301  
基金资助: 浙江省自然科学基金资助项目(LY13F020019).
通讯作者: 潘巧明,ORCID:http://orcid.org/0000-0002-2506-8293,E-mail:lsxypqm@163.com.      E-mail: lsxypqm@163.com
作者简介: 胡伟俭(1980-),ORCID:http://orcid.org/0000-0003-1299-878X,男,硕士,讲师,主要从事人机交互研究,E-mail:13754252004@163.com.
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引用本文:

胡伟俭, 陈为, 冯浩哲, 张天平, 朱正茂, 潘巧明. 应用于平扫CT图像肺结节检测的深度学习方法综述[J]. 浙江大学学报(理学版), 2017, 44(4): 379-384.

HU Weijian, CHEN Wei, FENG Haozhe, ZHANG Tianping, ZHU Zhengmao, PAN Qiaoming. A survey of depth learning methods for detecting lung nodules by CT images. Journal of Zhejiang University (Science Edition), 2017, 44(4): 379-384.

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https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2017.04.001        https://www.zjujournals.com/sci/CN/Y2017/V44/I4/379

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