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dc.contributor.authorTu, Xiaoguangen_US
dc.contributor.authorXie, Meien_US
dc.contributor.authorGao, Jingjingen_US
dc.contributor.authorMa, Zhengen_US
dc.contributor.authorChen, Daiqiangen_US
dc.contributor.authorWang, Qingfengen_US
dc.contributor.authorFinlayson, Samuel G.en_US
dc.contributor.authorOu, Yangmingen_US
dc.contributor.authorCheng, Jie-Zhien_US
dc.date.accessioned2017-12-05T23:52:31Z
dc.date.issued2017en_US
dc.identifier.citationTu, Xiaoguang, Mei Xie, Jingjing Gao, Zheng Ma, Daiqiang Chen, Qingfeng Wang, Samuel G. Finlayson, Yangming Ou, and Jie-Zhi Cheng. 2017. “Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network.” Scientific Reports 7 (1): 8533. doi:10.1038/s41598-017-08040-8. http://dx.doi.org/10.1038/s41598-017-08040-8.en
dc.identifier.issnen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34492080
dc.description.abstractWe present a computer-aided diagnosis system (CADx) for the automatic categorization of solid, part-solid and non-solid nodules in pulmonary computerized tomography images using a Convolutional Neural Network (CNN). Provided with only a two-dimensional region of interest (ROI) surrounding each nodule, our CNN automatically reasons from image context to discover informative computational features. As a result, no image segmentation processing is needed for further analysis of nodule attenuation, allowing our system to avoid potential errors caused by inaccurate image processing. We implemented two computerized texture analysis schemes, classification and regression, to automatically categorize solid, part-solid and non-solid nodules in CT scans, with hierarchical features in each case learned directly by the CNN model. To show the effectiveness of our CNN-based CADx, an established method based on histogram analysis (HIST) was implemented for comparison. The experimental results show significant performance improvement by the CNN model over HIST in both classification and regression tasks, yielding nodule classification and rating performance concordant with those of practicing radiologists. Adoption of CNN-based CADx systems may reduce the inter-observer variation among screening radiologists and provide a quantitative reference for further nodule analysis.en
dc.language.isoen_USen
dc.publisherNature Publishing Group UKen
dc.relation.isversionofdoi:10.1038/s41598-017-08040-8en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581338/pdf/en
dash.licenseLAAen_US
dc.titleAutomatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Networken
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalScientific Reportsen
dash.depositing.authorFinlayson, Samuel G.en_US
dc.date.available2017-12-05T23:52:31Z
dc.identifier.doi10.1038/s41598-017-08040-8*
dash.contributor.affiliatedFinlayson, Samuel
dash.contributor.affiliatedOu, Yangming


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