{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:39:49Z","timestamp":1730309989240,"version":"3.28.0"},"reference-count":23,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,13]]},"DOI":"10.1117\/12.2511438","type":"proceedings-article","created":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T22:59:42Z","timestamp":1552517982000},"page":"124","source":"Crossref","is-referenced-by-count":7,"title":["3D fully convolutional network-based segmentation of lung nodules in CT images with a clinically inspired data synthesis method"],"prefix":"10.1117","author":[{"given":"Atsushi","family":"Yaguchi","sequence":"first","affiliation":[]},{"given":"Kota","family":"Aoyagi","sequence":"first","affiliation":[]},{"given":"Akiyuki","family":"Tanizawa","sequence":"first","affiliation":[]},{"given":"Yoshiharu","family":"Ohno","sequence":"first","affiliation":[]}],"member":"189","reference":[{"issue":"6","key":"c1","first-page":"394","article-title":"Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"c2","article-title":"SEER cancer statistics review 1975-2015","author":"Noone","year":"2018","journal-title":"National Cancer Institute, Bethesda, MD"},{"key":"c3","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1102873"},{"key":"c4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015132766"},{"key":"c5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017151022"},{"key":"c6","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.178.5.1781053"},{"key":"c7","doi-asserted-by":"publisher","DOI":"10.5334\/jbr-btr.1202"},{"key":"c8","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"c9","first-page":"1752","article-title":"A multi-view deep convolutional neural networks for lung nodule segmentation","volume-title":"Proc. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Wang","year":"2017"},{"key":"c10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.014"},{"key":"c11","article-title":"Lung nodule segmentation with convolutional neural network trained by simple diameter information","volume-title":"Proc. International conference on Medical Imaging with Deep Learning (MIDL)","author":"Nam","year":"2018"},{"key":"c12","first-page":"1109","article-title":"Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction","volume-title":"Proc. IEEE International Symposium on Biomedical Imaging (ISBI)","author":"Wu","year":"2018"},{"key":"c13","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume-title":"Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)","author":"Ronneberger","year":"2015"},{"key":"c14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"c15","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume-title":"Proc. International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Glorot","year":"2011"},{"key":"c16","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"Proc. International Conference on Machine Learning (ICML)","author":"Ioffe","year":"2015"},{"key":"c17","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"c18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"c19","doi-asserted-by":"publisher","DOI":"10.1118\/1.3528204"},{"key":"c20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.828354"},{"key":"c21","first-page":"94142S","article-title":"Semiautomated segmentation of solid and ggo nodules in lung CT images using vessel-likelihood derived from local foreground structure","volume-title":"Proc. SPIE","volume":"9414","author":"Yaguchi","year":"2015"},{"key":"c22","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. Internationa! Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Glorot","year":"2010"},{"key":"c23","article-title":"Online learning rate adaptation with hypergradient descent","volume-title":"Proc. International Conference on Learning Representations (ICLR)","author":"Baydin","year":"2018"}],"event":{"name":"Computer-Aided Diagnosis","start":{"date-parts":[[2019,2,16]]},"location":"San Diego, United States","end":{"date-parts":[[2019,2,21]]}},"container-title":["Medical Imaging 2019: Computer-Aided Diagnosis"],"original-title":[],"deposited":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T16:54:09Z","timestamp":1554137649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/10950\/2511438\/3D-fully-convolutional-network-based-segmentation-of-lung-nodules-in\/10.1117\/12.2511438.full"}},"subtitle":[],"editor":[{"given":"Horst K.","family":"Hahn","sequence":"first","affiliation":[]},{"given":"Kensaku","family":"Mori","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,3,13]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1117\/12.2511438","relation":{},"subject":[],"published":{"date-parts":[[2019,3,13]]}}}