{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:42:01Z","timestamp":1732038121294},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T00:00:00Z","timestamp":1585008000000},"content-version":"am","delay-in-days":236,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA210360"],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1436827","gn 1722516"],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1016\/j.eswa.2019.01.048","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T09:13:30Z","timestamp":1547802810000},"page":"84-95","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":186,"special_numbering":"C","title":["An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"],"prefix":"10.1016","volume":"128","author":[{"given":"Shiwen","family":"Shen","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1001-4727","authenticated-orcid":false,"given":"Simon X","family":"Han","sequence":"additional","affiliation":[]},{"given":"Denise R","family":"Aberle","sequence":"additional","affiliation":[]},{"given":"Alex A","family":"Bui","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5168-070X","authenticated-orcid":false,"given":"William","family":"Hsu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2019.01.048_bib0001","series-title":"Osdi","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","volume":"16","author":"Abadi","year":"2016"},{"issue":"2","key":"10.1016\/j.eswa.2019.01.048_bib0002","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.acra.2015.10.014","article-title":"After detection: The improved accuracy of lung cancer assessment using radiologic computer-aided diagnosis","volume":"23","author":"Amir","year":"2016","journal-title":"Academic Radiology"},{"issue":"6","key":"10.1016\/j.eswa.2019.01.048_bib0003","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1118\/1.1573210","article-title":"Automated lung nodule classification following automated nodule detection on ct: A serial approach","volume":"30","author":"Armato","year":"2003","journal-title":"Medical Physics"},{"issue":"2","key":"10.1016\/j.eswa.2019.01.048_bib0004","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans","volume":"38","author":"Armato","year":"2011","journal-title":"Medical Physics"},{"issue":"Feb","key":"10.1016\/j.eswa.2019.01.048_bib0005","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2019.01.048_bib0006","unstructured":"Chollet, F. et\u00a0al. (2015). Keras."},{"issue":"1","key":"10.1016\/j.eswa.2019.01.048_bib0007","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.media.2015.08.001","article-title":"Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2d views and a convolutional neural network out-of-the-box","volume":"26","author":"Ciompi","year":"2015","journal-title":"Medical Image Analysis"},{"issue":"6","key":"10.1016\/j.eswa.2019.01.048_bib0008","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The cancer imaging archive (TCIA): Maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"Journal of Digital Imaging"},{"key":"10.1016\/j.eswa.2019.01.048_bib0009","series-title":"International workshop on energy minimization methods in computer vision and pattern recognition","first-page":"478","article-title":"A technique for lung nodule candidate detection in ct using global minimization methods","author":"Duggan","year":"2015"},{"issue":"1","key":"10.1016\/j.eswa.2019.01.048_bib0010","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1148\/radiographics.20.1.g00ja0343","article-title":"Solitary pulmonary nodules: Part i. morphologic evaluation for differentiation of benign and malignant lesions","volume":"20","author":"Erasmus","year":"2000","journal-title":"Radiographics"},{"key":"10.1016\/j.eswa.2019.01.048_bib0011","series-title":"Biomedical imaging: From nano to macro, 2011 IEEE international symposium on","first-page":"169","article-title":"Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose ct scans of the chest","author":"Farag","year":"2011"},{"issue":"1","key":"10.1016\/j.eswa.2019.01.048_bib0012","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12938-015-0120-7","article-title":"Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy","volume":"15","author":"Firmino","year":"2016","journal-title":"Biomedical Engineering Online"},{"key":"10.1016\/j.eswa.2019.01.048_bib0013","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.eswa.2016.10.039","article-title":"Lung nodule classification using artificial crawlers, directional texture and support vector machine","volume":"69","author":"Froz","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.048_bib0014","series-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"Glorot","year":"2010"},{"issue":"4","key":"10.1016\/j.eswa.2019.01.048_bib0015","doi-asserted-by":"crossref","first-page":"e1002277","DOI":"10.1371\/journal.pmed.1002277","article-title":"Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study","volume":"14","author":"ten Haaf","year":"2017","journal-title":"PLoS Medicine"},{"issue":"4","key":"10.1016\/j.eswa.2019.01.048_bib0016","doi-asserted-by":"crossref","first-page":"044504","DOI":"10.1117\/1.JMI.3.4.044504","article-title":"Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: Probing the lung image database consortium dataset with two statistical learning methods","volume":"3","author":"Hancock","year":"2016","journal-title":"Journal of Medical Imaging"},{"key":"10.1016\/j.eswa.2019.01.048_bib0017","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.eswa.2019.01.048_bib0018","article-title":"Computer-aided classification of lung nodules on computed tomography images via deep learning technique","volume":"8","author":"Hua","year":"2015","journal-title":"OncoTargets and Therapy"},{"key":"10.1016\/j.eswa.2019.01.048_bib0019","unstructured":"Huang, G., Liu, Z., Weinberger, K. Q., & van der Maaten, L. (2016). Densely connected convolutional networks. arXiv:1608.06993."},{"issue":"1","key":"10.1016\/j.eswa.2019.01.048_bib0020","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1148\/radiol.2017162725","article-title":"Added value of computer-aided ct image features for early lung cancer diagnosis with small pulmonary nodules: A matched case-control study","volume":"286","author":"Huang","year":"2017","journal-title":"Radiology"},{"year":"2015","series-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","key":"10.1016\/j.eswa.2019.01.048_bib0021"},{"issue":"2","key":"10.1016\/j.eswa.2019.01.048_bib0022","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.crad.2014.09.017","article-title":"Improving the radiologist\u2013cad interaction: Designing for appropriate trust","volume":"70","author":"Jorritsma","year":"2015","journal-title":"Clinical Radiology"},{"key":"10.1016\/j.eswa.2019.01.048_bib0023","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jbi.2015.05.011","article-title":"A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics","volume":"56","author":"Kaya","year":"2015","journal-title":"Journal of Biomedical Informatics"},{"issue":"9","key":"10.1016\/j.eswa.2019.01.048_bib0024","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1097\/RLI.0000000000000152","article-title":"Quantitative computed tomography imaging biomarkers in the diagnosis and management of lung cancer","volume":"50","author":"Kim","year":"2015","journal-title":"Investigative Radiology"},{"year":"2014","series-title":"Adam: A method for stochastic optimization","author":"Kingma","key":"10.1016\/j.eswa.2019.01.048_bib0025"},{"key":"10.1016\/j.eswa.2019.01.048_bib0026","series-title":"Advances in neural information processing systems","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.eswa.2019.01.048_bib0027","series-title":"Computer and robot vision (CRV), 2015 12th conference on","first-page":"133","article-title":"Lung nodule classification using deep features in CT images","author":"Kumar","year":"2015"},{"issue":"12","key":"10.1016\/j.eswa.2019.01.048_bib0028","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1016\/j.patcog.2013.06.017","article-title":"Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model","volume":"46","author":"Lin","year":"2013","journal-title":"Pattern Recognition"},{"issue":"12","key":"10.1016\/j.eswa.2019.01.048_bib0029","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1016\/j.acra.2007.07.021","article-title":"The lung image database consortium (LIDC) data collection process for nodule detection and annotation","volume":"14","author":"McNitt-Gray","year":"2007","journal-title":"Academic Radiology"},{"issue":"10","key":"10.1016\/j.eswa.2019.01.048_bib0030","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1056\/NEJMoa1214726","article-title":"Probability of cancer in pulmonary nodules detected on first screening CT","volume":"369","author":"McWilliams","year":"2013","journal-title":"New England Journal of Medicine"},{"key":"10.1016\/j.eswa.2019.01.048_bib0031","series-title":"Biomedical and health informatics (BHI), 2016 IEEE-EMBS international conference on","first-page":"380","article-title":"Assessing variability in brain tumor segmentation to improve volumetric accuracy and characterization of change","author":"Piedra","year":"2016"},{"key":"10.1016\/j.eswa.2019.01.048_bib0032","unstructured":"Reeves A.P., Biancardi A.M., (2011). The lung image database consortium (LIDC) nodule size report. http:\/\/www.via.cornell.edu\/lidc\/. Accessed 2018-06-01."},{"key":"10.1016\/j.eswa.2019.01.048_bib0033","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.compbiomed.2014.12.008","article-title":"An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy","volume":"57","author":"Shen","year":"2015","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2019.01.048_bib0035","series-title":"International conference on information processing in medical imaging","first-page":"588","article-title":"Multi-scale convolutional neural networks for lung nodule classification","author":"Shen","year":"2015"},{"key":"10.1016\/j.eswa.2019.01.048_bib0036","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","article-title":"Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification","volume":"61","author":"Shen","year":"2017","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2019.01.048_bib0037","article-title":"Very deep convolutional networks for large-scale image recognition","volume":"abs\/1409.1556","author":"Simonyan","year":"2014","journal-title":"CoRR"},{"issue":"1","key":"10.1016\/j.eswa.2019.01.048_bib0038","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"issue":"8","key":"10.1016\/j.eswa.2019.01.048_bib0039","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1001\/archinte.1997.00440290031002","article-title":"The probability of malignancy in solitary pulmonary nodules: Application to small radiologically indeterminate nodules","volume":"157","author":"Swensen","year":"1997","journal-title":"Archives of Internal Medicine"},{"issue":"365","key":"10.1016\/j.eswa.2019.01.048_bib0040","first-page":"395","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"2011","author":"Team","year":"2011","journal-title":"The New England Journal of Medicine"},{"key":"10.1016\/j.eswa.2019.01.048_bib0041","unstructured":"The Cancer Imaging Archive (2017). Lung image database consortium - reader annotation and markup - annotation and markup issues\/comments. Accessed 2018-06-01. https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/LIDC-IDRI."},{"key":"10.1016\/j.eswa.2019.01.048_bib0042","series-title":"Lung cancer and personalized medicine","first-page":"1","article-title":"Lung cancer statistics","author":"Torre","year":"2016"},{"issue":"7","key":"10.1016\/j.eswa.2019.01.048_bib0043","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.1118\/1.3140589","article-title":"Computer-aided diagnosis of pulmonary nodules on ct scans: Improvement of classification performance with nodule surface features","volume":"36","author":"Way","year":"2009","journal-title":"Medical Physics"},{"issue":"6","key":"10.1016\/j.eswa.2019.01.048_bib0044","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.ejrad.2013.02.018","article-title":"Exploring intra-and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on ct scans reconstructed at different slice intervals","volume":"82","author":"Zhao","year":"2013","journal-title":"European Journal of Radiology"},{"key":"10.1016\/j.eswa.2019.01.048_bib0045","series-title":"Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE","first-page":"4493","article-title":"Probabilistic lung nodule classification with belief decision trees","author":"Zinovev","year":"2011"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417419300545?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417419300545?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T15:57:36Z","timestamp":1618847856000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417419300545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":44,"alternative-id":["S0957417419300545"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2019.01.048","relation":{},"ISSN":["0957-4174"],"issn-type":[{"type":"print","value":"0957-4174"}],"subject":[],"published":{"date-parts":[[2019,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2019.01.048","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}