{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T11:57:13Z","timestamp":1793361433496,"version":"4.5.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T00:00:00Z","timestamp":1555545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1007\/s10278-018-0167-7","type":"journal-article","created":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T18:02:31Z","timestamp":1555610551000},"page":"672-677","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2647-6202","authenticated-orcid":false,"given":"Gene","family":"Kitamura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chul Y.","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"II","given":"Barry E.","family":"Moore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,4,18]]},"reference":[{"key":"167_CR1","doi-asserted-by":"crossref","unstructured":"Cao Y, Wang H, Moradi M, Prasanna P, Syeda-Mahmood TF: Fracture detection in x-ray images through stacked random forests feature fusion. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015. p. 801\u20135.","DOI":"10.1109\/ISBI.2015.7163993"},{"key":"167_CR2","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A: Deep Learning. Cambridge: MIT Press, 2016"},{"key":"167_CR3","unstructured":"Gale W, Oakden-Rayner L, Carneiro G, Bradley AP, Palmer LJ: Detecting hip fractures with radiologist-level performance using deep neural networks [Internet]. arXiv [cs.CV]. 2017. Available from: \n                    http:\/\/arxiv.org\/abs\/1711.06504\n                    \n                  . Accessed 9 Feb 2018"},{"key":"167_CR4","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ: Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Internet]. 2017. Available from: \n                    https:\/\/doi.org\/10.1109\/cvpr.2017.243\n                    \n                  . Accessed 9 Feb 2018","DOI":"10.1109\/cvpr.2017.243"},{"key":"167_CR5","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, et al: Caffe: Convolutional Architecture for Fast Feature Embedding [Internet]. arXiv [cs.CV]. 2014. Available from: \n                    http:\/\/arxiv.org\/abs\/1408.5093\n                    \n                  . Accessed 9 Feb 2018","DOI":"10.1145\/2647868.2654889"},{"key":"167_CR6","first-page":"1097","volume-title":"Advances in Neural Information Processing Systems 25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: ImageNet Classification with Deep Convolutional Neural Networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ Eds. Advances in Neural Information Processing Systems 25. New York: Curran Associates, Inc, 2012, pp. 1097\u20131105"},{"key":"167_CR7","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","volume":"88","author":"J Olczak","year":"2017","unstructured":"Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, Sk\u00f6ldenberg O, Gordon M: Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 88:581\u2013586, 2017","journal-title":"Acta Orthop"},{"key":"167_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V: Ioffe - \u2026 on Computer Vision \u2026 S, 2016. Rethinking the inception architecture for computer vision. \n                    cv-foundation.org\n                    \n                   [Internet]. 2016; Available from: \n                    http:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2016\/html\/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.html\n                    \n                  . Accessed 9 Feb 2018","DOI":"10.1109\/CVPR.2016.308"},{"key":"167_CR9","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Internet]. 2016. Available from: \n                    https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/cvpr.2016.90"},{"key":"167_CR10","unstructured":"Chollet F: Xception: Deep Learning with Depthwise Separable Convolutions [Internet]. arXiv [cs.CV]. 2016. Available from: \n                    http:\/\/arxiv.org\/abs\/1610.02357\n                    \n                  . Accessed 9 Feb 2018"},{"key":"167_CR11","doi-asserted-by":"crossref","unstructured":"Mason - Medical Physics D: SU-E-T-33: Pydicom: An Open Source DICOM Library. Wiley Online Library [Internet]. 2011; Available from: \n                    http:\/\/onlinelibrary.wiley.com\/doi\/10.1118\/1.3611983\/full\n                    \n                  . Accessed 9 Feb 2018","DOI":"10.1118\/1.3611983"},{"key":"167_CR12","unstructured":"Jones E, Oliphant T: Peterson - URL \n                    http:\/\/scipy.org\n                    \n                   P, 2011. SciPy: Open source scientific tools for Python, 2009. 2011"},{"key":"167_CR13","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Identity Mappings in Deep Residual Networks. Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J: Identity Mappings in Deep Residual Networks. Computer Vision \u2013 ECCV 2016. Cham: Springer, 2016, pp. 630\u2013645"},{"key":"167_CR14","unstructured":"Ju C, Bibaut A, van der Laan MJ: The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification [Internet]. arXiv [stat.ML]. 2017. Available from: \n                    http:\/\/arxiv.org\/abs\/1704.01664\n                    \n                  . Accessed 9 Feb 2018"},{"key":"167_CR15","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al.: Scikit-learn: Machine Learning in Python. J Mach Learn Res 12:2825\u20132830, 2011","journal-title":"J Mach Learn Res"},{"key":"167_CR16","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems [Internet]. arXiv [cs.DC]. 2016. Available from: \n                    http:\/\/arxiv.org\/abs\/1603.04467\n                    \n                  . Accessed 9 Feb 2018"},{"key":"167_CR17","unstructured":"Li Z, Wang C, Han M, Xue Y, Wei W, Li L-J, et al: Thoracic Disease Identification and Localization with Limited Supervision [Internet]. arXiv [cs.CV]. 2017. Available from: \n                    http:\/\/arxiv.org\/abs\/1711.06373\n                    \n                  . Accessed 9 Feb 2018"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-018-0167-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-018-0167-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-018-0167-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,16]],"date-time":"2020-04-16T19:19:02Z","timestamp":1587064742000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-018-0167-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,18]]},"references-count":17,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["167"],"URL":"https:\/\/doi.org\/10.1007\/s10278-018-0167-7","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,18]]},"assertion":[{"value":"18 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}