Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

被引：14

作者：

Zhang P. ^{[1
]}

Xu X. ^{[2
]}

Wang H. ^{[1
]}

Feng Y. ^{[3
]}

Feng H. ^{[3
]}

Zhang J. ^{[3
]}

Yan S. ^{[3
]}

Hou Y. ^{[3
]}

Song Y. ^{[3
]}

Li J. ^{[3
]}

Liu X. ^{[3
]}

机构：

[1] School of Economics and Management, Tongji University, Shanghai

[2] Tongji University Affiliated to Shanghai Pulmonary Hospital Thoracic Surgery, Shanghai

[3] State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2018年 / 30卷 / 01期

关键词：

Deep learning; Lung cancer; Medical image processing;

D O I：

10.3724/SP.J.1089.2018.16919

中图分类号：

学科分类号：

摘要：

Since 21th century, cancer is one of the high rates of mortality in the world, while lung cancer is at the top for both mortality and morbidity among all the cancers. With the development of big data and artificial intelligence, relying on deep learning to help diagnose lung cancer has become a hot topic in recent years. The key to computer-aided lung cancer diagnosis is mainly to process and analyze lung images, which was summarized as 4 steps: medical image data preprocessing, lung segmentation, lung nodule detection and segmentation, and pathological diagnosis. Deep learning on lung cancer diagnosis mainly focuses on lung segmentation, lung nodule detection and pathological analysis. This is because deep learning techniques rely strongly on large amount of training data, while current public data is mainly CT images annotated for lung nodules. This paper overviews classical medical image processing algorithms for auxiliary lung cancer diagnosis, and summarizes state-of-art deep-learning-based medical image processing methods. © 2018, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：90 / 99

页数：9

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