{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:22:15Z","timestamp":1750220535357,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,5,29]]},"DOI":"10.1145\/3468920.3468930","type":"proceedings-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T16:54:06Z","timestamp":1629824046000},"page":"70-75","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bone Age Assessment Based on Deep Convolution Neural Network"],"prefix":"10.1145","author":[{"given":"Shoujian","family":"Yu","sequence":"first","affiliation":[{"name":"Donghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianbang","family":"Ge","sequence":"additional","affiliation":[{"name":"Donghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolin","family":"Xia","sequence":"additional","affiliation":[{"name":"Donghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,24]]},"reference":[{"volume-title":"Radiographic atlas of skeletal development of the hand and wrist","author":"Bayer L. M. .","key":"e_1_3_2_1_1_1"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Morris L. L. . 2003. Assessment of skeletal maturity and prediction of adult height (tw3 method). Australasian Radiology. https:\/\/doi.org\/10.1046\/j.1440-1673.2003.01196.x  Morris L. L. . 2003. Assessment of skeletal maturity and prediction of adult height (tw3 method). Australasian Radiology. https:\/\/doi.org\/10.1046\/j.1440-1673.2003.01196.x","DOI":"10.1046\/j.1440-1673.2003.01196.x"},{"key":"e_1_3_2_1_3_1","unstructured":"Kingma D. and Ba J. . 2014. Adam: a method for stochastic optimization. Computer Science.  Kingma D. and Ba J. . 2014. Adam: a method for stochastic optimization. Computer Science."},{"volume-title":"Dual Attention Network for Scene Segmentation. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.","author":"Fu J.","key":"e_1_3_2_1_4_1"},{"key":"e_1_3_2_1_5_1","article-title":"Squeeze-and-excitation networks","author":"Jie H.","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(99)."},{"key":"e_1_3_2_1_6_1","first-page":"2818","article-title":"Rethinking the Inception Architecture for Computer Vision","author":"Szegedy C.","year":"2016","journal-title":"IEEE"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0734-189X(87)80186-X"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"volume-title":"Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society. https:\/\/doi.org\/10","year":"2016","author":"He K.","key":"e_1_3_2_1_9_1"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Li X. Sun X. Meng Y. Liang J. Wu F. and Li J. . 2019. Dice loss for data-imbalanced nlp tasks.  Li X. Sun X. Meng Y. Liang J. Wu F. and Li J. . 2019. Dice loss for data-imbalanced nlp tasks.","DOI":"10.18653\/v1\/2020.acl-main.45"},{"volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. https:\/\/doi.org\/10","author":"Ronneberger O.","key":"e_1_3_2_1_11_1"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Iglovikov V. Rakhlin A. Kalinin A. and Shvets A. . 2017. Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks.  Iglovikov V. Rakhlin A. Kalinin A. and Shvets A. . 2017. Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks.","DOI":"10.1101\/234120"},{"key":"e_1_3_2_1_13_1","first-page":"0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"2017","author":"Ching T.","year":"2018","journal-title":"Journal of the Royal Society Interface, 15(141). https:\/\/doi.org\/10.1098\/rsif."},{"issue":"6","key":"e_1_3_2_1_14_1","first-page":"788","article-title":"Assessment of skeletal maturity and prediction of adult height (tw2 method)","volume":"14","author":"Morris L. L. .","year":"1976","journal-title":"American Journal of Human Biology"},{"key":"e_1_3_2_1_15_1","first-page":"1","article-title":"H-denseunet: hybrid densely connected unet for liver and liver tumor segmentation from ct volumes","author":"Li X.","year":"2017","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.926067"},{"volume-title":"Training Models of Shape from Sets of Examples","author":"Cootes","key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","DOI":"10.5244\/C.6.2"},{"key":"e_1_3_2_1_18_1","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot X.","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"e_1_3_2_1_20_1","unstructured":"Simonyan K. and Zisserman A. . 2014. Very deep convolutional networks for large-scale image recognition. Computer Science  Simonyan K. and Zisserman A. . 2014. Very deep convolutional networks for large-scale image recognition. Computer Science"}],"event":{"name":"BDE 2021: The 2021 3rd International Conference on Big Data Engineering","acronym":"BDE 2021","location":"Shanghai China"},"container-title":["The 2021 3rd International Conference on Big Data Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3468920.3468930","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3468920.3468930","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:02Z","timestamp":1750195502000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3468920.3468930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,29]]},"references-count":20,"alternative-id":["10.1145\/3468920.3468930","10.1145\/3468920"],"URL":"https:\/\/doi.org\/10.1145\/3468920.3468930","relation":{},"subject":[],"published":{"date-parts":[[2021,5,29]]},"assertion":[{"value":"2021-08-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}