{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:40:57Z","timestamp":1775821257825,"version":"3.50.1"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52250005"],"award-info":[{"award-number":["52250005"]}],"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":["21875271"],"award-info":[{"award-number":["21875271"]}],"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":["21707147"],"award-info":[{"award-number":["21707147"]}],"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":["11604346"],"award-info":[{"award-number":["11604346"]}],"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":["21671195"],"award-info":[{"award-number":["21671195"]}],"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":["51872302"],"award-info":[{"award-number":["51872302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key R&D Projects of Zhejiang Province","award":["2022C01236"],"award-info":[{"award-number":["2022C01236"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1901001"],"award-info":[{"award-number":["2019YFB1901001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province Key Research and Development Program","award":["2019C01060"],"award-info":[{"award-number":["2019C01060"]}]},{"name":"key technology for virtue reactors from NPIC Entrepreneurship Program of Foshan National Hi-tech Industrial Development Zone","award":["2015ZX06004-001"],"award-info":[{"award-number":["2015ZX06004-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3305269","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T17:56:40Z","timestamp":1692035800000},"page":"87398-87408","source":"Crossref","is-referenced-by-count":8,"title":["Semi-Supervised Metallographic Image Segmentation via Consistency Regularization and Contrastive Learning"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3766-8246","authenticated-orcid":false,"given":"Fan","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaolin","family":"Guo","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[{"name":"Material Science and Chemical Engineering, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6707-3915","authenticated-orcid":false,"given":"Shiyu","family":"Du","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-20037-5"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/ma15134417"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_30"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3059505"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref31","article-title":"Temporal ensembling for semi-supervised learning","author":"laine","year":"2016","journal-title":"arXiv 1610 02242"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1017\/S1431927618015635"},{"key":"ref11","article-title":"Semi-supervised learning by entropy minimization","volume":"17","author":"grandvalet","year":"2004","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3066850"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/AMM.152-154.276"},{"key":"ref1","first-page":"8","article-title":"An improved iterative watershed according to ridge detection for segmentation of metallographic image","volume":"8","author":"liu","year":"2012","journal-title":"Metallogr Image"},{"key":"ref17","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"2013","journal-title":"Proc Workshop Challenges Represent Learn (ICML)"},{"key":"ref16","article-title":"Adversarial learning for semi-supervised semantic segmentation","author":"hung","year":"2018","journal-title":"arXiv 1802 07934"},{"key":"ref19","article-title":"Improved regularization of convolutional neural networks with cutout","author":"devries","year":"2017","journal-title":"arXiv 1708 04552"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00421"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00811"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref22","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"tarvainen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref28","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume":"33","author":"xie","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","article-title":"Representation learning with contrastive predictive coding","author":"van den oord","year":"2018","journal-title":"arXiv 1807 03748"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.09.018"},{"key":"ref8","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","volume":"29","author":"sajjadi","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref9","first-page":"11525","article-title":"Dash: Semi-supervised learning with dynamic thresholding","author":"xu","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.28.3.033035"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICISCE.2015.121"},{"key":"ref6","article-title":"Semi-supervised semantic segmentation needs strong, varied perturbations","author":"french","year":"2019","journal-title":"arXiv 1906 01916"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10216984.pdf?arnumber=10216984","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T18:28:39Z","timestamp":1694456919000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10216984\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3305269","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}