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Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification
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
2017
发表期刊PATTERN RECOGNITION
卷号61期号:61页码:663-673
文章类型Article
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.
关键词Lung Nodule Malignancy Suspiciousness Convolutional Neural Network Multi-crop Pooling
WOS标题词Science & Technology ; Technology
DOI10.1016/j.patcog.2016.05.029
关键词[WOS]IMAGE DATABASE CONSORTIUM ; COMPUTER-AIDED DIAGNOSIS ; PULMONARY NODULES ; CT IMAGES ; SEGMENTATION ; CANCER ; REPRESENTATION ; INFORMATION ; ENSEMBLE ; SCANS
收录类别SCI
语种英语
项目资助者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)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000385899400051
引用统计
被引频次:41[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12244
专题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
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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.
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