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Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

文献类型：期刊论文

作者Shen, Wei1,2; Zhou, Mu3; Yang, Feng4; Yu, Dongdong1,2; Dong, Di1,2; Yang, Caiyun1,2; Zang, Yali1,2; Tian, Jie1,2; Feng Yang, Jie Tian
刊名PATTERN RECOGNITION
出版日期2017
卷号61期号:61页码:663-673
关键词Lung Nodule Malignancy Suspiciousness Convolutional Neural Network Multi-crop Pooling
DOI10.1016/j.patcog.2016.05.029
文献子类Article
英文摘要We investigate the problem of lung nodule malignancy suspiciousness (the likelihood of nodule malignancy) classification using thoracic Computed Tomography (CT) images. Unlike traditional studies primarily relying on cautious nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw nodule patches and building an end-to-end machine-learning architecture for classifying lung nodule malignancy suspiciousness. We present a Multi-crop Convolutional Neural Network (MC-CNN) to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times. Extensive experimental results show that the proposed method not only achieves state-of-the-art nodule suspiciousness classification performance, but also effectively characterizes nodule semantic attributes (subtlety and margin) and nodule diameter which are potentially helpful in modeling nodule malignancy; We investigate the problem of lung nodule malignancy suspiciousness (the likelihood of nodule malignancy) classification using thoracic Computed Tomography (CT) images. Unlike traditional studies primarily relying on cautious nodule segmentation and time-consuming feature extraction, we tackle a more challenging task on directly modeling raw nodule patches and building an end-to-end machine learning architecture for classifying lung nodule malignancy suspiciousness. We present a Multi-crop Convolutional Neural Network (MC-CNN) to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times. Extensive experimental results show that the proposed method not only achieves state-of-the-art nodule suspiciousness classification performance, but also effectively characterizes nodule semantic attributes (subtlety and margin) and nodule diameter which are potentially helpful in modeling nodule malignancy. (C) 2016 Elsevier Ltd. All rights reserved.
WOS关键词IMAGE DATABASE CONSORTIUM ; COMPUTER-AIDED DIAGNOSIS ; PULMONARY NODULES ; CT IMAGES ; SEGMENTATION ; CANCER ; REPRESENTATION ; INFORMATION ; ENSEMBLE ; SCANS
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000385899400051
资助机构Chinese Academy of Sciences Key Deployment Program(KGZD-EW-T03) ; National Natural Science Foundation of China(81227901 ; Beijing Natural Science Foundation(4132080) ; Fundamental Research Funds for the Central Universities(2013JBZ014 ; Scientific Research and Equipment Development Project of Chinese Academy of Sciences(YZ201457) ; NVIDIA Corporation ; 81527805 ; 2016JBM018) ; 61231004 ; 81370035 ; 81230030 ; 61301002 ; 61302025 ; 81301346 ; 81501616)
源URL[http://ir.ia.ac.cn/handle/173211/12244]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Feng Yang, Jie Tian
作者单位1.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Stanford Univ, Stanford Ctr Biomed Informat Res, Stanford, CA 94305 USA
4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
推荐引用方式
GB/T 7714		 Shen, Wei,Zhou, Mu,Yang, Feng,et al. Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification[J]. PATTERN RECOGNITION,2017,61(61):663-673.
APA Shen, Wei.,Zhou, Mu.,Yang, Feng.,Yu, Dongdong.,Dong, Di.,...&Feng Yang, Jie Tian.(2017).Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification.PATTERN RECOGNITION,61(61),663-673.
MLA Shen, Wei,et al."Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification".PATTERN RECOGNITION 61.61(2017):663-673.

入库方式： OAI收割

来源：自动化研究所

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