{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T19:02:46Z","timestamp":1769540566704,"version":"3.49.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1931202"],"award-info":[{"award-number":["U1931202"]}],"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":["62076033"],"award-info":[{"award-number":["62076033"]}],"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":["30700349"],"award-info":[{"award-number":["30700349"]}],"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":["30440012"],"award-info":[{"award-number":["30440012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science and Technology Commission","doi-asserted-by":"publisher","award":["Z201100007520001"],"award-info":[{"award-number":["Z201100007520001"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science and Technology Commission","doi-asserted-by":"publisher","award":["Z131100004013036"],"award-info":[{"award-number":["Z131100004013036"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"publisher"}]},{"name":"BUPT Excellent Ph.D. Students Foundation","award":["CX2019217"],"award-info":[{"award-number":["CX2019217"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1109\/jbhi.2020.3043589","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T18:50:26Z","timestamp":1607539826000},"page":"2673-2685","source":"Crossref","is-referenced-by-count":6,"title":["Triple Up-Sampling Segmentation Network With Distribution Consistency Loss for Pathological Diagnosis of Cervical Precancerous Lesions"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7519-4124","authenticated-orcid":false,"given":"Zhu","family":"Meng","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6506-7298","authenticated-orcid":false,"given":"Zhicheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1247-8979","authenticated-orcid":false,"given":"Bingyang","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4245-4687","authenticated-orcid":false,"given":"Fei","family":"Su","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limei","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Basic Medical Sciences, Third Hospital, Peking University Health Science Center, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5913-9897","authenticated-orcid":false,"given":"Haiying","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"279","article-title":"Brain tumor segmentation using an ensemble of 3d U-nets and overall survival prediction using radiomic features","year":"2018","journal-title":"International MICCAI Brainlesion Workshop"},{"key":"ref38","first-page":"506","article-title":"Automatic brain tumor detection and segmentation using u-net based fully convolutional networks","year":"2017","journal-title":"Proc"},{"key":"ref33","first-page":"369","article-title":"Multi-scale cell instance segmentation with keypoint graph based bounding boxes","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Interv"},{"key":"ref32","first-page":"378","article-title":"Improving nuclei\/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss","year":"2019","journal-title":"Proc"},{"key":"ref31","first-page":"2487","article-title":"DCAN: Deep contour-aware networks for accurate gland segmentation","year":"2016","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93000-8_87"},{"key":"ref37","first-page":"5886","article-title":"ENS-Unet: End-to-end noise suppression u-net for brain tumor segmentation","year":"2018","journal-title":"Proc 40th Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"ref36","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","year":"2015","journal-title":"Proc"},{"key":"ref35","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref34","first-page":"351","article-title":"Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier","year":"2019","journal-title":"Proc"},{"key":"ref28","first-page":"804","article-title":"Classification of breast cancer histology image using ensemble of pre-trained neural networks","year":"2018","journal-title":"Proc Int Conf Image Anal Recognit"},{"key":"ref27","first-page":"1030","article-title":"Multi-classification of breast cancer histology images by using gravitation loss","year":"2019","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93000-8_106"},{"key":"ref2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"ref20","first-page":"1595","article-title":"Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification","volume":"20","year":"2015","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref22","first-page":"385","article-title":"Adversarial neural networks for basal membrane segmentation of microinvasive cervix carcinoma in histopathology images","year":"2017","journal-title":"Proc Int Conf Mach Learn Cybern"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.4018\/IJHISI.2019040105"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2496264"},{"key":"ref26","first-page":"541","article-title":"Pancreatic cancer detection in whole slide images using noisy label annotations","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Interv"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2665602"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref51","first-page":"1055","article-title":"UNet 3+: A full-scale connected unet for medical image segmentation","year":"2020","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process"},{"key":"ref10","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","year":"2018","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref11","year":"0"},{"key":"ref40","first-page":"1441","article-title":"U2-net: A bayesian U-net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans","year":"2019","journal-title":"Proc IEEE Int Symp Biomed Imag"},{"key":"ref12","year":"0"},{"key":"ref13","year":"0"},{"key":"ref14","first-page":"1","article-title":"Pap-smear benchmark data for pattern classification","author":"jantzen","year":"2005","journal-title":"Nature Inspired Smart Inf Syst"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/JBHI.2016.2519686","article-title":"Evaluation of three algorithms for the segmentation of overlapping cervical cells","volume":"21","author":"zhi","year":"2017","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2389619"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2705583"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2915633"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2013.08.001"},{"key":"ref4","first-page":"770","article-title":"Deep residual learning for image recognition","year":"2016","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref3","first-page":"1","article-title":"Going deeper with convolutions","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_93"},{"key":"ref5","year":"0"},{"key":"ref8","author":"kumar","year":"2014","journal-title":"Robbins and Cotran pathologic basis of disease professional edition e-book"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01080"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3390\/rs12071128"},{"key":"ref9","first-page":"976","article-title":"Adaptive elastic loss based on progressive inter-class association for cervical histology image segmentation","year":"2020","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref48","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","year":"2015","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref42","first-page":"84","article-title":"Dual encoding U-net for retinal vessel segmentation","year":"2019","journal-title":"Proc"},{"key":"ref41","first-page":"788","article-title":"DME-Net: Diabetic macular edema grading by auxiliary task learning","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Interv"},{"key":"ref44","first-page":"614","article-title":"MSU-net: Multiscale statistical U-net for real-time 3d cardiac MRI video segmentation","year":"2019","journal-title":"Int Conf Med Image Comput Comput -Assisted Interv"},{"key":"ref43","first-page":"721","article-title":"CS-net: Channel and spatial attention network for curvilinear structure segmentation","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Interv"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9497060\/09288896.pdf?arnumber=9288896","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T05:57:21Z","timestamp":1769493441000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9288896\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":51,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2020.3043589","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}