{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:16:16Z","timestamp":1730308576332,"version":"3.28.0"},"reference-count":23,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,3,3]]},"DOI":"10.1117\/12.2250360","type":"proceedings-article","created":{"date-parts":[[2017,3,3]],"date-time":"2017-03-03T20:31:36Z","timestamp":1488573096000},"page":"101343G","source":"Crossref","is-referenced-by-count":1,"title":["Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images"],"prefix":"10.1117","volume":"10134","author":[{"given":"Yunzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Univ. of Oklahoma (United States)"}]},{"given":"Yuchen","family":"Qiu","sequence":"additional","affiliation":[{"name":"Univ. of Oklahoma (United States)"}]},{"given":"Theresa","family":"Thai","sequence":"additional","affiliation":[{"name":"Health Science Ctr. of Univ. of Oklahoma (United States)"}]},{"given":"Kathleen","family":"Moore","sequence":"additional","affiliation":[{"name":"Health Science Ctr. of Univ. of Oklahoma (United States)"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"Univ. of Oklahoma (United States)"}]},{"given":"Bin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Univ. of Oklahoma (United States)"}]}],"member":"189","reference":[{"key":"c1","doi-asserted-by":"publisher","DOI":"10.1210\/jcem-54-2-254"},{"key":"c2","doi-asserted-by":"publisher","DOI":"10.1053\/gast.2001.22430"},{"key":"c3","doi-asserted-by":"publisher","DOI":"10.1161\/ATVBAHA.107.159228"},{"key":"c4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2014.01.031"},{"key":"c5","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.211.1.r99ap15283"},{"key":"c6","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-016-0157-5"},{"key":"c7","doi-asserted-by":"publisher","DOI":"10.3892\/ol.2016.4648"},{"issue":"11","key":"c8","first-page":"2278","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998"},{"key":"c9","doi-asserted-by":"publisher","DOI":"10.1109\/72.554195"},{"article-title":"Detection of concealed cars in complex cargo X-ray imagery using deep learning","year":"2016","author":"Jaccard","key":"c10"},{"key":"c11","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"c12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2524985"},{"key":"c13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2532122"},{"key":"c14","first-page":"520","article-title":"A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations.","author":"Roth","year":"2014"},{"key":"c15","first-page":"978520","article-title":"Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study","volume":"9785","author":"Qiu","year":"2016"},{"key":"c16","first-page":"978521","article-title":"An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology","volume":"9785","author":"Qiu","year":"2016"},{"key":"c17","first-page":"97090K","article-title":"Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation","volume":"9709","author":"Qiu","year":"2016"},{"key":"c18","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(03)00380-5"},{"key":"c19","doi-asserted-by":"publisher","DOI":"10.3233\/XST-160595"},{"article-title":"Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis","year":"2015","author":"Qiu","key":"c20"},{"key":"c21","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2477688"},{"key":"c22","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2473823"},{"key":"c23","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25276"}],"event":{"name":"SPIE Medical Imaging","location":"Orlando, Florida, United States"},"container-title":["SPIE Proceedings","Medical Imaging 2017: Computer-Aided Diagnosis"],"original-title":[],"deposited":{"date-parts":[[2018,9,27]],"date-time":"2018-09-27T04:41:31Z","timestamp":1538023291000},"score":1,"resource":{"primary":{"URL":"http:\/\/proceedings.spiedigitallibrary.org\/proceeding.aspx?doi=10.1117\/12.2250360"}},"subtitle":[],"editor":[{"given":"Samuel G.","family":"Armato","sequence":"first","affiliation":[{"name":"The Univ. of Chicago (United States)"}]},{"given":"Nicholas A.","family":"Petrick","sequence":"additional","affiliation":[{"name":"U.S. Food and Drug Administration (United States)"}]}],"short-title":[],"issued":{"date-parts":[[2017,3,3]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1117\/12.2250360","relation":{},"ISSN":["0277-786X"],"issn-type":[{"type":"print","value":"0277-786X"}],"subject":[],"published":{"date-parts":[[2017,3,3]]}}}