{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T13:57:51Z","timestamp":1754488671397,"version":"3.37.3"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"23","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFE0126100"],"award-info":[{"award-number":["2018YFE0126100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702275","41775008"],"award-info":[{"award-number":["61702275","41775008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2021,12,1]]},"DOI":"10.1109\/jiot.2021.3068775","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T20:43:05Z","timestamp":1616704985000},"page":"16902-16910","source":"Crossref","is-referenced-by-count":7,"title":["Applying Cross-Modality Data Processing for Infarction Learning in Medical Internet of Things"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8253-1686","authenticated-orcid":false,"given":"Chenchu","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-4439","authenticated-orcid":false,"given":"Zhifan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2948-1384","authenticated-orcid":false,"given":"Dong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7499-1992","authenticated-orcid":false,"given":"Jinglin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2013.6590059"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2015.7293298"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.10.041"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.csr.2008.04.011"},{"journal-title":"L2 regularization versus batch and weight normalization","year":"2017","author":"van laarhoven","key":"ref30"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref36","first-page":"1","article-title":"Adam: Amethod for stochastic optimization","author":"kingma","year":"2014","journal-title":"Proc 3rd Int Conf Learn Represent"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_28"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/SOCA.2015.38"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1800110"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2015150033"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.109.865352"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11091882"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00234-016-1658-1"},{"journal-title":"Combining multi-sequence and synthetic images for improved segmentation of late gadolinium enhancement cardiac MRI","year":"2019","author":"campello","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1186\/1532-429X-16-S1-P362"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_39"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.09.001"},{"key":"ref19","article-title":"Spatio-temporal multi-task learning for cardiac MRI left ventricle quantification","author":"vesal","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref28","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput Assist Intervent"},{"key":"ref4","first-page":"2430","article-title":"Label-less learning for emotion cognition","volume":"31","author":"chen","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"journal-title":"Non-local neural networks","year":"2017","author":"wang","key":"ref27"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICII.2019.00079"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC.2019.8666599"},{"key":"ref29","first-page":"5767","article-title":"Improved training of wasserstein gans","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.03.054"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2991578"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.26583\/sv.11.4.05"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.34"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2964412"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.09.024"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.ahj.2011.12.002","article-title":"Renal failure and acute myocardial infarction: Clinical characteristics in patients with advanced chronic kidney disease, on dialysis, and without chronic kidney disease. A collaborative project of the United States Renal Data System\/National Institutes of Health and the National Registry of Myocardial Infarction","volume":"163","author":"shroff","year":"2012","journal-title":"Amer Heart J"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.01.004"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC47757.2020.9049689"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/kisup.2015.2"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2955436"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2746879"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI45749.2020.9098613"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.1900070"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1348\/000712603762842093"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101554"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2019.1800419"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_59"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3043289"},{"key":"ref25","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/9620058\/09386256.pdf?arnumber=9386256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:35Z","timestamp":1652194415000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9386256\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,1]]},"references-count":48,"journal-issue":{"issue":"23"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2021.3068775","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"type":"electronic","value":"2327-4662"},{"type":"electronic","value":"2372-2541"}],"subject":[],"published":{"date-parts":[[2021,12,1]]}}}