{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:27:28Z","timestamp":1775064448323,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Korea Institute of Radiological and Medical Sciences funded by Ministry of Science and ICT","award":["50543-2019"],"award-info":[{"award-number":["50543-2019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s10278-020-00362-w","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T17:02:41Z","timestamp":1595523761000},"page":"1202-1208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology"],"prefix":"10.1007","volume":"33","author":[{"given":"Soon Woo","family":"Kwon","sequence":"first","affiliation":[]},{"given":"Ik Joon","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Ju Yong","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Won Il","family":"Jang","sequence":"additional","affiliation":[]},{"given":"Guk-Haeng","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2574-4976","authenticated-orcid":false,"given":"Myung-Chul","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"362_CR1","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1002\/hed.25029","volume":"40","author":"A Sanabria","year":"2018","unstructured":"Sanabria A, Kowalski LP, Shah JP, Nixon IJ, Angelos P, Williams MD, Rinaldo A, Ferlito A: Growing incidence of thyroid carcinoma in recent years: factors underlying overdiagnosis. Head Neck 40:855-866,2018","journal-title":"Head Neck"},{"key":"362_CR2","doi-asserted-by":"crossref","unstructured":"Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini Furio, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L: 2015 American Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer,2016","DOI":"10.1089\/thy.2015.0020"},{"key":"362_CR3","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1148\/radiol.11110206","volume":"260","author":"JY Kwak","year":"2011","unstructured":"Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, Jung HK, Choi JS, Kim BM, Kim EK: Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology 260:892-899,2011","journal-title":"Radiology"},{"key":"362_CR4","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s13244-013-0256-6","volume":"4","author":"RK Lingam","year":"2013","unstructured":"Lingam RK, Qarib MH, Tolley NS: Evaluating thyroid nodules: predicting and selecting malignant nodules for fine-needle aspiration (FNA) cytology. Insights Imaging 4:617-624,2013","journal-title":"Insights Imaging"},{"key":"362_CR5","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1089\/thy.2016.0372","volume":"27","author":"YJ Choi","year":"2017","unstructured":"Choi YJ, Baek JH, Park HS, Shim WH, Kim TY, Shong YK, Lee JH: A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment. Thyroid 27:546-552, 2017","journal-title":"Thyroid"},{"key":"362_CR6","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.ultras.2011.11.003","volume":"52","author":"UR Acharya","year":"2012","unstructured":"Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS: Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan systems. Ultrasonics 52:508-520,2012","journal-title":"Ultrasonics"},{"key":"362_CR7","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.cmpb.2011.10.001","volume":"107","author":"UR Acharya","year":"2012","unstructured":"Acharya UR, Faust O, Sree SV, Molinari F, Suri JS: ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Comput Methods Prog Biomed 107:233-241,2012","journal-title":"Comput Methods Prog Biomed"},{"key":"362_CR8","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.ultras.2016.09.011","volume":"73","author":"J Ma","year":"2017","unstructured":"Ma J, Wu F, Zhu J, Xu D, Kong D: A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 73:221-230,2017","journal-title":"Ultrasonics"},{"key":"362_CR9","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.neuroimage.2017.07.018","volume":"180","author":"K Seeliger","year":"2018","unstructured":"Seeliger K, Fritsche M, Guclu U, Schoenmakers S, Schoffelen JM, Bosch SE, van Gerven MAJ: Convolutional neural network-based encoding and decoding of visual object recognition in space and time. Neuroimage 180:253-266,2018","journal-title":"Neuroimage"},{"key":"362_CR10","doi-asserted-by":"publisher","first-page":"2362108","DOI":"10.1155\/2018\/2362108","volume":"2018","author":"AA Nahid","year":"2018","unstructured":"Nahid AA, Mehrabi MA, Kong Y: Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed Res Int 2018:2362108,2018","journal-title":"Biomed Res Int"},{"key":"362_CR11","first-page":"6215085","volume":"2016","author":"W Li","year":"2016","unstructured":"Li W, Cao P, Zhao D, Wang J: Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Methods Med 2016:6215085,2016","journal-title":"Comput Math Methods Med"},{"key":"362_CR12","first-page":"520","volume":"17","author":"HR Roth","year":"2014","unstructured":"Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. Med Image Comput Comput Assist Interv 17:520-527,2014","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"362_CR13","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/s10278-017-9997-y","volume":"30","author":"J Chi","year":"2017","unstructured":"Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M: Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 30:477-486,2017","journal-title":"J Digit Imaging"},{"key":"362_CR14","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt S, Schonberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T, Scikit-image contributors: Scikit-image: image processing in Python. PeerJ 2:e453,2014","journal-title":"PeerJ"},{"key":"362_CR15","doi-asserted-by":"crossref","unstructured":"Damelin S, Hoang N: On surface completion and image inpainting by biharmonic functions: numerical aspects. Int J Math Math Sci 2018,2018","DOI":"10.1155\/2018\/3950312"},{"key":"362_CR16","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1109\/TIP.2010.2044962","volume":"19","author":"PC Tay","year":"2010","unstructured":"Tay PC, Garson CD, Acton ST, Hossack JA: Ultrasound despeckling for contrast enhancement. IEEE Trans Image Process 19:1847-1860,2010","journal-title":"IEEE Trans Image Process"},{"key":"362_CR17","first-page":"187","volume":"9","author":"F Benzarti","year":"2012","unstructured":"Benzarti F, Amiri H. Speckle noise reduction in medical ultrasound images. Int J Comput Sci Issues 9:187\u201394,2012","journal-title":"Int J Comput Sci Issues"},{"key":"362_CR18","unstructured":"Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition. In ICLR,2015"},{"key":"362_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M: Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211-252, 2015","journal-title":"Int J Comput Vis"},{"key":"362_CR20","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35:1240-1251,2016. https:\/\/doi.org\/10.1109\/TMI.2016.2538465. Epub 2532016 Mar 2538464","journal-title":"IEEE Trans Med Imaging"},{"key":"362_CR21","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.diii.2018.11.001","volume":"100","author":"M Colevray","year":"2019","unstructured":"Colevray M, Tatard-Leitman VM, Gouttard S, Douek P, Boussel L: Convolutional neural network evaluation of over-scanning in lung computed tomography. Diagn Interv Imaging 100:177-183,2019","journal-title":"Diagn Interv Imaging"},{"key":"362_CR22","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1002\/hed.25415","volume":"41","author":"SY Ko","year":"2019","unstructured":"Ko SY, Lee JH, Yoon JH, Na H, Hong E, Han K, Jung I, Kim EK, Moon HJ, Park VY, Lee E, Kwak JY: Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head Neck 41:885-891,2019","journal-title":"Head Neck"},{"key":"362_CR23","doi-asserted-by":"crossref","unstructured":"Wong SC, Gatt A, Stamatescu V, McDonnell MD: Understanding data augmentation for classification: when to warp?. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016, pp 1-6","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"362_CR24","doi-asserted-by":"publisher","first-page":"370","DOI":"10.3348\/kjr.2016.17.3.370","volume":"17","author":"JH Shin","year":"2016","unstructured":"Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, Lim HK, Moon WJ, Na DG, Park JS, Choi YJ, Hahn SY, Jeon SJ, Jung SL, Kim DW, Kim EK, Kwak JY, Lee CY, Lee HJ, Lee JH, Lee JH, Lee KH, Park SW, Sung JY, Korean Society of Thyroid Radiology, Korean Society of Radiology: Ultrasonography diagnosis and imaging-based management of thyroid nodules: revised Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J Radiol 17:370-395,2016","journal-title":"Korean J Radiol"},{"key":"362_CR25","unstructured":"Lin M, Chen Q, Yan S: Network in network. In ICLR,2014"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-020-00362-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-020-00362-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-020-00362-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T23:49:09Z","timestamp":1626997749000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-020-00362-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["362"],"URL":"https:\/\/doi.org\/10.1007\/s10278-020-00362-w","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]},"assertion":[{"value":"23 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}