Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification
We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotati...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2018-01, Vol.22 (1), p.184-195 |
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| container_title | IEEE journal of biomedical and health informatics |
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| creator | Wang, Qiangchang Zheng, Yuanjie Yang, Gongping Jin, Weidong Chen, Xinjian Yin, Yilong |
| description | We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art. |
| doi_str_mv | 10.1109/JBHI.2017.2685586 |
| format | Article |
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(IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-18309db5577404a09a04e03fc75298b3277484bd21159050fe97898d34e05c863</citedby><cites>FETCH-LOGICAL-c415t-18309db5577404a09a04e03fc75298b3277484bd21159050fe97898d34e05c863</cites><orcidid>0000-0003-0416-8778 ; 0000-0002-5186-6504</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7883849$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7883849$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28333649$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Qiangchang</creatorcontrib><creatorcontrib>Zheng, Yuanjie</creatorcontrib><creatorcontrib>Yang, Gongping</creatorcontrib><creatorcontrib>Jin, Weidong</creatorcontrib><creatorcontrib>Chen, Xinjian</creatorcontrib><creatorcontrib>Yin, Yilong</creatorcontrib><title>Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification</title><title>IEEE journal of biomedical and health informatics</title><addtitle>JBHI</addtitle><addtitle>IEEE J Biomed Health Inform</addtitle><description>We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. 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| subjects | Artificial neural networks Biomedical imaging Computed tomography Convolutional neural network (CNN) Feature extraction gabor filter Image analysis Image processing Informatics interstitial lung disease (ILD) classification Invariants local binary pattern (LBP) lung classification Lung diseases Lungs Neural networks Support vector machines |
| title | Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification |
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