CASIA OpenIR  > 中国科学院分子影像重点实验室
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
Source PublicationPATTERN RECOGNITION
2017
Volume61Issue:61Pages:663-673
SubtypeArticle
AbstractWe 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.
KeywordLung Nodule Malignancy Suspiciousness Convolutional Neural Network Multi-crop Pooling
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2016.05.029
WOS KeywordIMAGE DATABASE CONSORTIUM ; COMPUTER-AIDED DIAGNOSIS ; PULMONARY NODULES ; CT IMAGES ; SEGMENTATION ; CANCER ; REPRESENTATION ; INFORMATION ; ENSEMBLE ; SCANS
Indexed BySCI
Language英语
Funding OrganizationChinese 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 Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000385899400051
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12244
Collection中国科学院分子影像重点实验室
Corresponding AuthorFeng Yang, Jie Tian
Affiliation1.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
Recommended Citation
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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