{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T20:49:32Z","timestamp":1782161372619,"version":"3.54.5"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3700401","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:56:52Z","timestamp":1780603012000},"page":"90386-90398","source":"Crossref","is-referenced-by-count":0,"title":["Noisy Teacher-Student Knowledge Distillation for Efficient Mass Detection in Mammography"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0737-1325","authenticated-orcid":false,"given":"Khai-Thinh","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bach-Tung","family":"Pham","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1095-170X","authenticated-orcid":false,"given":"Chanh-Nghiem","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Automation Engineering, Can Tho University, Can Tho, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6659-2481","authenticated-orcid":false,"given":"Thi-Phuong","family":"Le","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9241-6633","authenticated-orcid":false,"given":"Huang-Chia","family":"Shih","sequence":"additional","affiliation":[{"name":"Department of Information Management, National Central University, Taoyuan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Chiung","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-3689","authenticated-orcid":false,"given":"Yung-Hui","family":"Li","sequence":"additional","affiliation":[{"name":"AI Research Center, Hon Hai Research Institute, New Taipei City, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chien-Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Academia Sinica, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0024-6732","authenticated-orcid":false,"given":"Jia-Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Global Cancer Observatory: Cancer Today (Version 1.1)","author":"Ferlay","year":"2024"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa1600249"},{"key":"ref3","volume-title":"Survival Rates for Breast Cancer","year":"2026"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106903"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108211","article-title":"Improved PAA algorithm for breast mass detection in mammograms","volume":"251","author":"Liu","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"issue":"14","key":"ref6","doi-asserted-by":"crossref","first-page":"20043","DOI":"10.1007\/s11042-022-12332-1","article-title":"Mammogram breast cancer CAD systems for mass detection and classification: A review","volume":"81","author":"Hassan","year":"2022","journal-title":"Multimedia Tools Appl."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.6.3.031409"},{"issue":"1","key":"ref8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.jksuci.2022.11.006","article-title":"Multiple-level thresholding for breast mass detection","volume":"35","author":"Yu","year":"2023","journal-title":"J. King Saud Univ. - Comput. Inf. Sci."},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103774","article-title":"Deep learning for mass detection in full field digital mammograms","volume":"121","author":"Agarwal","year":"2020","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"ref10","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.bbe.2021.03.005","article-title":"Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention","volume":"41","author":"Yan","year":"2021","journal-title":"Biocybernetics Biomed. Eng."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-023-10189-6"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103192","article-title":"BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations","volume":"96","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11153-1"},{"issue":"3","key":"ref14","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1007\/s12652-023-04517-9","article-title":"Transformer-based mass detection in digital mammograms","volume":"14","author":"Tarifa","year":"2023","journal-title":"J. Ambient Intell. Humanized Comput."},{"key":"ref15","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Van Engelen","year":"2019","journal-title":"Mach. Learn."},{"issue":"1","key":"ref16","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-023-02025-1","article-title":"An online mammography database with biopsy confirmed types","volume":"10","author":"Cai","year":"2023","journal-title":"Sci. Data"},{"key":"ref17","volume-title":"RSNA Screening Mammography Breast Cancer Detection","year":"2026"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.neucom.2020.09.037","article-title":"Weakly and semi supervised detection in medical imaging via deep dual branch net","volume":"421","author":"Bakalo","year":"2021","journal-title":"Neurocomputing"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00385"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"issue":"3","key":"ref22","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.jfma.2022.09.014","article-title":"The COVIDTW study: Clinical predictors of COVID-19 mortality and a novel AI prognostic model using chest X-ray","volume":"122","author":"Wu","year":"2022","journal-title":"J. Formosan Med. Assoc."},{"issue":"3","key":"ref23","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.injury.2020.09.010","article-title":"Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques","volume":"52","author":"Aghnia Farda","year":"2021","journal-title":"Injury"},{"issue":"4","key":"ref24","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.3390\/s22041586","article-title":"Convolutional blur attention network for cell nuclei segmentation","volume":"22","author":"Le","year":"2022","journal-title":"Sensors"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00745"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/HealthCom60970.2024.10880812"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15102704"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2577031"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2023.3238524"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2011.09.014"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.177"},{"key":"ref38","doi-asserted-by":"crossref","DOI":"10.1007\/978-94-011-5318-8_75","article-title":"Current status of the digital database for screening mammography","volume-title":"Digital Mammography: Nijmegen, 1998","author":"Heath","year":"1998"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref40","article-title":"2013 ACR BI-RADS Atlas: Breast imaging reporting and data system","author":"D\u2019Orsi","year":"2014"},{"key":"ref41","first-page":"1","article-title":"Mini-DDSM: Mammography-based automatic age estimation","volume-title":"Proc. 3rd Int. Conf. Digit. Med. Image Process.","author":"Lekamlage"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934"},{"key":"ref43","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref45","first-page":"646","article-title":"Deep networks with stochastic depth","volume-title":"Proc. Eur. Conf. Comput. Vis.","author":"Huang"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.220047.podcast"},{"issue":"6","key":"ref47","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1109\/TPAMI.2021.3055564","article-title":"Knowledge distillation and student\u2013teacher learning for visual intelligence: A review and new outlooks","volume":"44","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3099856"},{"key":"ref49","first-page":"1","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. ICML Workshop","author":"Lee"},{"key":"ref50","first-page":"1","article-title":"Rethinking pre-training and self-training","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Zoph"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref52","volume-title":"Breast Cancer Signs and Symptoms","year":"2026"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350535"},{"issue":"5","key":"ref54","first-page":"713","article-title":"A fast algorithm for multilevel thresholding","volume":"17","author":"Liao","year":"2001","journal-title":"J. Inf. Sci. Eng."},{"key":"ref55","article-title":"Gridmask data augmentation","author":"Chen","year":"2024","journal-title":"arXiv:2001.04086"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref57","article-title":"Decoupled weight decay regularization","volume-title":"Proc. 7th Int. Conf. Learn. Represent.","author":"Loshchilov"},{"key":"ref58","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","volume-title":"Proc. 30th Int. Conf. Int. Conf. Mach. Learn.","author":"Pascanu"},{"key":"ref59","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Ioffe"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref61","article-title":"YOLOv12: Attention-centric real-time object detectors","volume-title":"Proc. Thirty-ninth Annu. Conf. Neural Inf. Process. Syst.","author":"Tian"},{"key":"ref62","article-title":"RT-DETRv4: Painlessly furthering real-time object detection with vision foundation models","author":"Liao","year":"2025","journal-title":"arXiv:2510.25257"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11551578.pdf?arnumber=11551578","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T19:58:06Z","timestamp":1782158286000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11551578\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":62,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3700401","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}