{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:59:20Z","timestamp":1783439960746,"version":"3.54.6"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"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":["62171377"],"award-info":[{"award-number":["62171377"]}],"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":["62271405"],"award-info":[{"award-number":["62271405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningbo Clinical Research Center for Medical Imaging","award":["2021L003 (Open Project 2022LYKFZD06)"],"award-info":[{"award-number":["2021L003 (Open Project 2022LYKFZD06)"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of Ningbo City, China","doi-asserted-by":"publisher","award":["2021J052"],"award-info":[{"award-number":["2021J052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1109\/tmi.2024.3357986","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T18:39:34Z","timestamp":1706121574000},"page":"2180-2190","source":"Crossref","is-referenced-by-count":12,"title":["Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification"],"prefix":"10.1109","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7345-2794","authenticated-orcid":false,"given":"Hongyu","family":"Wang","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"He","sequence":"additional","affiliation":[{"name":"Huiying Medical Technology Company Ltd., Haidian District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8625-2521","authenticated-orcid":false,"given":"Hengfei","family":"Cui","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Yuan","sequence":"additional","affiliation":[{"name":"Sichuan Provincial Health Information Center (Sichuan Provincial Health and Medical Big Data Center), Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9273-2847","authenticated-orcid":false,"given":"Yong","family":"Xia","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-008-0948-3"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/emp2.12297"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref4","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Goldberger"},{"key":"ref5","article-title":"Robust inference via generative classifiers for handling noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16437-8_52"},{"key":"ref7","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref8","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref9","article-title":"SELF: Learning to filter noisy labels with self-ensembling","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Nguyen"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref11","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Zhang"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3000949"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3042773"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180921"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.1095"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87762-2"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102225"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2877939"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3180545"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_19"},{"key":"ref24","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref25","article-title":"Masking: A new perspective of noisy supervision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102087"},{"key":"ref27","article-title":"Decoupling \u2018when to update\u2019 from \u2018how to update","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Malach"},{"key":"ref28","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref29","article-title":"Curriculum loss: Robust learning and generalization against label corruption","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Lyu"},{"key":"ref30","article-title":"Searching to exploit memorization effect in learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yao"},{"key":"ref31","first-page":"1","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Berthelot"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/455"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref34","article-title":"Using trusted data to train deep networks on labels corrupted by severe noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Hendrycks"},{"key":"ref35","first-page":"6226","article-title":"Peer loss functions: Learning from noisy labels without knowing noise rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref36","article-title":"LDMI: A novel information-theoretic loss function for training deep nets robust to label noise","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Xu"},{"key":"ref37","article-title":"When optimizing f-divergence is robust with label noise","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wei"},{"key":"ref38","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881970-018"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"ref42","article-title":"Energy-based learning for cooperative games, with applications to valuation problems in machine learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bian"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/778"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482302"},{"key":"ref45","article-title":"SWAD: Domain generalization by seeking flat minima","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Cha"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3390\/e22020221"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinam.2019.100064"},{"key":"ref48","article-title":"A family of statistical symmetric divergences based on Jensen\u2019s inequality","author":"Nielsen","year":"2010","journal-title":"arXiv:1009.4004"},{"key":"ref49","first-page":"11720","article-title":"Diversity and generalization in neural network ensembles","volume-title":"Proc. 25th Int. Conf. Artif. Intell. Statist.","volume":"151","author":"Ortega"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-93967-2"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101797"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43898-1_32"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"ref58","volume-title":"Deep Learning for Medical Image Interpretation","author":"Rajpurkar","year":"2021"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.2307\/2289144"},{"key":"ref61","article-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10547147\/10413624.pdf?arnumber=10413624","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T19:28:47Z","timestamp":1735154927000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10413624\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":61,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2024.3357986","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}