{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:56:03Z","timestamp":1775256963142,"version":"3.50.1"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004921","name":"Shanghai Jiao Tong University","doi-asserted-by":"publisher","award":["YG2017ZD08"],"award-info":[{"award-number":["YG2017ZD08"]}],"id":[{"id":"10.13039\/501100004921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81471733"],"award-info":[{"award-number":["81471733"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61471390"],"award-info":[{"award-number":["61471390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2017YFC0107602"],"award-info":[{"award-number":["2017YFC0107602"]}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["16410722400"],"award-info":[{"award-number":["16410722400"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1109\/jbhi.2018.2876916","type":"journal-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T18:29:58Z","timestamp":1539973798000},"page":"2030-2038","source":"Crossref","is-referenced-by-count":107,"title":["Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3903-1333","authenticated-orcid":false,"given":"Xuhua","family":"Ren","sequence":"first","affiliation":[]},{"given":"Tingting","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiujun","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3730-6401","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7243-9977","authenticated-orcid":false,"given":"Sahar","family":"Ahmad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0763-2313","authenticated-orcid":false,"given":"Lei","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Shaun Richard","family":"Stone","sequence":"additional","affiliation":[]},{"given":"Lihong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yiqiang","family":"Zhan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7934-5698","authenticated-orcid":false,"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3490-3836","authenticated-orcid":false,"given":"Qian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"4278","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"0","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref33","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref32","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"0","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref31","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"0","journal-title":"Proc 32nd Int Conf Mach Learn Proc Mach Learn Res"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref37","article-title":"Keras: Deep learning library for theano and tensorflow","author":"chollet","year":"2015"},{"key":"ref36","article-title":"The effectiveness of data augmentation in image classification using deep learning","author":"wang","year":"2017"},{"key":"ref35","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2007.02.012"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/S0895-6111(00)00026-4"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1049\/cp:19971008","article-title":"automated vision system for skeletal age assessment using knowledge based techniques","volume":"2","author":"mahmoodi","year":"1997","journal-title":"Proceedings of 6th International Conference on Image Processing and its Applications (Conf Publ No 443)"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2004.01.002"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.926067"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/4233.897061"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.010"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1101\/234120","article-title":"Pediatric bone age assessment using deep convolutional neural networks","author":"iglovikov","year":"2017"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref4","first-page":"340","article-title":"Assessment of skeletal maturity and prediction of adult height (TW3 Method)","volume":"47","author":"morris","year":"2003","journal-title":"Journal of Medical Imaging and Radiation Oncology"},{"key":"ref27","first-page":"2087","article-title":"Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior","author":"kr?ah","year":"0","journal-title":"Proc IEEE Int Symp Biomed Imag From Nano Macro"},{"key":"ref3","first-page":"282","article-title":"Radiographic atlas of skeletal development of the hand and wrist","volume":"11","author":"garn","year":"1959","journal-title":"Amer J Human Genetics"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-007-0656-1"},{"key":"ref29","article-title":"Network in network","author":"lin","year":"2013"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2008.08.005"},{"key":"ref8","first-page":"6551","article-title":"Epiphysis and metaphysis extraction and classification by adaptive thresholding and DoG filtering for automated skeletal bone age analysis","author":"giordano","year":"0","journal-title":"Proc Annu Int Conf Eng Med Biol Soc"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1259\/0007-1285-67-801-848"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4103\/2230-8210.145076"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/42.938240"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2265.1995.tb01899.x"},{"key":"ref20","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"0","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Intervention"},{"key":"ref22","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2013.6738468"},{"key":"ref41","article-title":"Gradual Tuning: A better way of fine tuning the parameters of a deep neural network","author":"montone","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref26","first-page":"130","article-title":"Multiscale vessel enhancement filtering","author":"frangi","year":"0","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Intervention"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/8824156\/08500181.pdf?arnumber=8500181","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T21:08:47Z","timestamp":1657746527000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8500181\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9]]},"references-count":41,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2018.2876916","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9]]}}}