{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:27:46Z","timestamp":1765232866977,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["N171903003","N151903001","N171902001"],"award-info":[{"award-number":["N171903003","N151903001","N171902001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["81671773, 61672146","61802055"],"award-info":[{"award-number":["81671773, 61672146","61802055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"crossref","award":["20180550182"],"award-info":[{"award-number":["20180550182"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NIH Grant of the National Cancer Institute","award":["CA206171"],"award-info":[{"award-number":["CA206171"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s10278-019-00306-z","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T18:02:56Z","timestamp":1583517776000},"page":"685-696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9299-0915","authenticated-orcid":false,"given":"Fangfang","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linkai","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junxin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueyang","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouliang","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Moore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengrong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,6]]},"reference":[{"issue":"1","key":"306_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21332","volume":"66","author":"Rebecca L. Siegel","year":"2016","unstructured":"Siegel RL, Miller KD, Jemal A: Cancer statistics, 2016. CA: A Cancer Journal for Clinicians 67(1):7\u201330, 2016. https:\/\/doi.org\/10.3322\/caac.21332","journal-title":"CA: A Cancer Journal for Clinicians"},{"issue":"7","key":"306_CR2","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/S1470-2045(16)00162-5","volume":"17","author":"Annalisa Trama","year":"2016","unstructured":"Trama A, Botta L, Foschi R, Ferrari A, Stiller C, Desandes E, Maule MM, Merletti F, Gatta G, the EUROCARE-5 working group: Survival of European adolescents and young adults diagnosed with cancer in 2000\u201307: Population-based data from EUROCARE-5. The Lancet Oncology 17(7):896\u2013906, 2016. https:\/\/doi.org\/10.1016\/S1470-2045(16)00162-5","journal-title":"The Lancet Oncology"},{"issue":"10125","key":"306_CR3","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1016\/S0140-6736(17)33326-3","volume":"391","author":"Claudia Allemani","year":"2018","unstructured":"Allemani C, Matsuda T, Carlo VD, Harewood R, Matz M, Nik\u0161i\u0107 M, Bonaventure A, Valkov M, Johnson CJ, Est\u00e8ve J, Ogunbiyi OJ, Silva GAE, Chen WQ, Eser S, Engholm G, Stiller CA, Monnereau A, Woods RR, Visser O, Lim GH, Aitken J, Weir HK, Coleman MP, the CONCORD working group: Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): Analysis of individual records for 37513025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. The Lancet 391(10125):1023\u20131075, 2018. https:\/\/doi.org\/10.1016\/S0140-6736(17)33326-3","journal-title":"The Lancet"},{"key":"306_CR4","unstructured":"American Cancer Society: Cancer facts & figures 2019. Atlanta: American Cancer Society, 2019. Available at https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/cancer-facts-figures-2019.html"},{"issue":"1","key":"306_CR5","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21551","volume":"69","author":"Rebecca L. Siegel","year":"2019","unstructured":"Siegel RL, Miller KD, Jemal A: Cancer statistics, 2019. CA: A Cancer Journal for Clinicians 69(1):7\u201334, 2019. https:\/\/doi.org\/10.3322\/caac.21551","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"306_CR6","unstructured":"Gary Clayman. Thyroid nodules: Hyperthyroidism and thyroid Cancer. Endocrineweb, November 27th, 2018. Available at https:\/\/www.endocrineweb.com\/conditions\/thyroid\/thyroid-nodules"},{"issue":"5","key":"306_CR7","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1016\/j.compmedimag.2008.04.001","volume":"32","author":"Shingo Iwano","year":"2008","unstructured":"Iwano S, Nakamura T, Kamioka Y, Ishigaki T: Computer-aided diagnosis: A shape classification of pulmonary nodules imaged by high-resolution CT. Computerized Medical Imaging and Graphics 29(7):565\u2013570, 2005. https:\/\/doi.org\/10.1016\/j.compmedimag.2008.04.001","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"3","key":"306_CR8","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.lungcan.2007.06.018","volume":"58","author":"Hajime Saito","year":"2007","unstructured":"Saito H, Minamiya Y, Kawai H, Nakagawa T, Ito M, Hosono Y, Motoyama S, Hashimoto M, Ishiyama K, Ogawa JI. Usefulness of circumference difference for estimating the likelihood of malignancy in small solitary pulmonary nodules on CT. Lung Cancer 58(3): 348-354, 2007. https:\/\/doi.org\/10.1016\/j.lungcan.2007.06.018","journal-title":"Lung Cancer"},{"key":"306_CR9","first-page":"772","volume-title":"Lecture Notes in Computer Science","author":"Ayman El-Baz","year":"2011","unstructured":"El\u2013Baz A, Nitzken M, Khalifa F, Elnakib A, Gimel\u2019farb G, Falk R, and El-Ghar MA. 3D shape analysis for early diagnosis of malignant lung nodules. Information Processing in Medical Imaging 2011;22:772\u2013783. https:\/\/doi.org\/10.1007\/978-3-642-22092-0_63"},{"key":"306_CR10","doi-asserted-by":"publisher","unstructured":"El\u2013Baz A, Nitzken M, Vanbogaert E, Gimel\u2019farb G, Falk R, El-Ghar MA: A novel shape-based diagnostic approach for early diagnosis of lung nodules. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA, 30 March-2 April, 2011. https:\/\/doi.org\/10.1109\/ISBI.2011.5872373","DOI":"10.1109\/ISBI.2011.5872373"},{"issue":"1","key":"306_CR11","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1148\/radiology.217.1.r00oc33251","volume":"217","author":"David F. Yankelevitz","year":"2000","unstructured":"Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI: Small pulmonary nodules: Volumetrically determined growth rates based on CT evaluation. Radiology 217(1):251\u2013256, 2000. https:\/\/doi.org\/10.1148\/radiology.217.1.r00oc33251","journal-title":"Radiology"},{"issue":"10","key":"306_CR12","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1109\/TMI.2003.817785","volume":"22","author":"W.J. Kostis","year":"2003","unstructured":"Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI: Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Transactions on Medical Imaging 22(10):1259\u20131274, 2003. https:\/\/doi.org\/10.1109\/TMI.2003.817785","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"306_CR13","doi-asserted-by":"publisher","unstructured":"Prasanna P, Tiwari P, Madabhushi A: Co-occurrence of local anisotropic gradient orientations (CoL1AGe): A new radiomics descriptor. Scientific Reports 6:37241, 2016. https:\/\/doi.org\/10.1038\/srep37241","DOI":"10.1038\/srep37241"},{"key":"306_CR14","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B: Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 20-25 June, 2005. https:\/\/doi.org\/10.1109\/CVPR.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"issue":"7","key":"306_CR15","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T. Ojala","year":"2002","unstructured":"Ojala T, Pietik\u00e4inen M, M\u00e4enp\u00e4\u00e4 T: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and. Machine Intelligence 24(7):971\u2013987, 2002. https:\/\/doi.org\/10.1109\/TPAMI.2002.1017623","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"306_CR16","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s10278-014-9718-8","volume":"28","author":"Fangfang Han","year":"2014","unstructured":"Han F, Wang H, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z: Texture feature analysis for computer-aided diagnosis on pulmonary nodules. Journal of Digital Imaging 28(1):99\u2013115, 2015. https:\/\/doi.org\/10.1007\/s10278-014-9718-8","journal-title":"Journal of Digital Imaging"},{"issue":"2","key":"306_CR17","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3233\/XST-17302","volume":"26","author":"Huafeng Wang","year":"2018","unstructured":"Wang H, Zhao T, Li LC, Pan H, Liu W, Gao H, Han F, Wang Y, Qi Y, Liang Z: A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation. Journal of X-Ray Science and Technology 26(2):171\u2013187, 2018. https:\/\/doi.org\/10.3233\/XST-17302","journal-title":"Journal of X-Ray Science and Technology"},{"issue":"4","key":"306_CR18","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.compbiomed.2012.12.004","volume":"43","author":"Mohsen Keshani","year":"2013","unstructured":"Keshani M, Azimifa Z, Tajeripour F, Boostani R: Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system. Computers in Biology and Medicine 43(4):287\u2013300, 2013. https:\/\/doi.org\/10.1016\/j.compbiomed.2012.12.004","journal-title":"Computers in Biology and Medicine"},{"key":"306_CR19","doi-asserted-by":"crossref","unstructured":"Tartar A, Kilic N, Akan A: A new method for pulmonary nodule detection using decision trees. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, 3\u20137 July 2013. https:\/\/doi.org\/10.1109\/EMBC.2013.6611257","DOI":"10.1109\/EMBC.2013.6611257"},{"issue":"7","key":"306_CR20","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.compmedimag.2010.03.006","volume":"34","author":"S.L.A. Lee","year":"2010","unstructured":"Lee SLA, Kouzani A, Hu EJ: Random forest based lung nodule classification aided by clustering. Computerized Medical Imaging and Graphics 34(7):535\u2013542, 2010. https:\/\/doi.org\/10.1016\/j.compmedimag.2010.03.006","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"306_CR21","unstructured":"Yann L: LeNet-5, convolutional neural networks . NY, USA. [Online], 2013 Available at https:\/\/yann.lecun.com\/exdb\/lenet\/"},{"key":"306_CR22","doi-asserted-by":"publisher","unstructured":"Zhang T, Zhao J, Luo J, Qiang Y: Deep belief network for lung nodules diagnosed in CT imaging. International Journal of Performability Engineering 13(8):1358\u20131370, 2017. https:\/\/doi.org\/10.23940\/ijpe.17.08.p17.13581370","DOI":"10.23940\/ijpe.17.08.p17.13581370"},{"key":"306_CR23","doi-asserted-by":"publisher","unstructured":"Cheng J, Ni D, Chou Y, Qin J, Tiu C, Chang Y, Huang C, Shen D, Chen C: Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific Reports 6:24454, 2016. https:\/\/doi.org\/10.1038\/srep24454","DOI":"10.1038\/srep24454"},{"key":"306_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/8314740","volume":"2017","author":"QingZeng Song","year":"2017","unstructured":"Song Q, Zhao L, Luo X, Dou X: Using deep learning for classification of lung nodules on computed tomography images. Journal of Healthcare Engineering 2017(1):1\u20137, 2017. https:\/\/doi.org\/10.1155\/2017\/8314740","journal-title":"Journal of Healthcare Engineering"},{"key":"306_CR25","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.compbiomed.2017.04.006","volume":"89","author":"Wenqing Sun","year":"2017","unstructured":"Sun W, Zheng B, Qian W: Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Computers in Biology and Medicine 89:530\u2013539, 2017. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.04.006","journal-title":"Computers in Biology and Medicine"},{"issue":"2","key":"306_CR26","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"Samuel G. Armato","year":"2011","unstructured":"Armato, III SG, Mclennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP-Y, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical Physics 38(2):915\u2013931, 2011. https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Medical Physics"},{"key":"306_CR27","unstructured":"Jacobs C, Setio AAA, Traverso A, Ginneken BV: Lung nodule analysis 2016. [Online], 2016. Available at https:\/\/luna16.grand-challenge.org\/home\/"},{"issue":"5","key":"306_CR28","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"Hoo-Chang Shin","year":"2016","unstructured":"Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM: Deep donvolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35(5):1285\u20131298, 2016. https:\/\/doi.org\/10.1109\/TMI.2016.2528162","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"306_CR29","doi-asserted-by":"publisher","unstructured":"Hosny KM, Kassem MA, Foaud MM: Skin cancer classification using deep learning and transfer learning. 9th Cairo International Biomedical Engineering Conference (CIBEC2018), Cairo, Egypt, December 20\u201322, 2018. https:\/\/doi.org\/10.1109\/CIBEC.2018.8641762","DOI":"10.1109\/CIBEC.2018.8641762"},{"issue":"5","key":"306_CR30","doi-asserted-by":"publisher","first-page":"e0217293","DOI":"10.1371\/journal.pone.0217293","volume":"14","author":"Khalid M. Hosny","year":"2019","unstructured":"Hosny KM, Kassem MA, Foaud MM: Classification of skin lesions using transfer learning and augmentation with Alex-net. PLOS ONE 14(5):e0217293, 2019. https:\/\/doi.org\/10.1371\/journal.pone.0217293","journal-title":"PLOS ONE"},{"issue":"6","key":"306_CR31","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"Alex Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Communications of the ACM 60(6):84\u201390, June 2017. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Communications of the ACM"},{"key":"306_CR32","unstructured":"Simonyan K and Zisserman A: Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations 2015, San Diego, CA, May 7\u20139, 2015. Available at https:\/\/arxiv.org\/pdf\/1409.1556.pdf"},{"key":"306_CR33","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, and Rabinovich A: Going deeper with convolutions. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, June 7-12, 2015:1\u20139. https:\/\/doi.org\/10.1109\/cvpr.2015.7298594","DOI":"10.1109\/cvpr.2015.7298594"},{"key":"306_CR34","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, Las Vegas, NV, USA, June 27-30, 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10","key":"306_CR35","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"Sinno Jialin Pan","year":"2010","unstructured":"Pan SJ, Yang Q: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10):1345\u20131359, 2010. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"306_CR36","unstructured":"Glorot X, Bengio Y: Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249\u2013256, 2010. Available at\u00a0http:\/\/proceedings.mlr.press\/v9\/glorot10a\/glorot10a.pdf"},{"key":"306_CR37","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I and Salakhutdinov RR: Improving neural networks by preventing co-adaptation of feature detectors. [Online]. arXiv: 1207. 0580 [cs. NE], 2012. Available at https:\/\/arxiv.org\/pdf\/1207.0580.pdf"},{"key":"306_CR38","unstructured":"Ioffe S, Szegedy C: Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, July 2015, 37:448\u2013456. Available at http:\/\/arxiv.org\/abs\/1502.03167.pdf"},{"key":"306_CR39","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H: How transferable are features in deep neural networks? Proceedings of the 27th International Conference on Neural Information Processing Systems, December 2014, 2:3320-3328. Available at\u00a0https:\/\/papers.nips.cc\/paper\/5347-how-transferable-are-features-in-deep-neural-networks.pdf"},{"key":"306_CR40","doi-asserted-by":"publisher","unstructured":"Zeiler MD, Fergus R: Visualizing and understanding convolutional networks. European Conference on Computer Vision, Zurich, Switzerland 2014:818\u2013833. https:\/\/doi.org\/10.1007\/978-3-319-10590-1-53","DOI":"10.1007\/978-3-319-10590-1-53"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00306-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-019-00306-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00306-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T01:06:01Z","timestamp":1614992761000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-019-00306-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,6]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["306"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00306-z","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"type":"print","value":"0897-1889"},{"type":"electronic","value":"1618-727X"}],"subject":[],"published":{"date-parts":[[2020,3,6]]},"assertion":[{"value":"6 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}