{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:53:15Z","timestamp":1782816795385,"version":"3.54.5"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Research Grant from Shenzhen Municipal Central Government Guides Local Science and Technology Development Special Funded Projects","award":["2021Szvup139"],"award-info":[{"award-number":["2021Szvup139"]}]},{"name":"Research Grant from the Hong Kong Research Grants Council (RGC) through the National Nature Science Foundation of China\/Research Grants Council Joint Research Scheme","award":["N_HKUST627\/20"],"award-info":[{"award-number":["N_HKUST627\/20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1109\/tmi.2021.3137854","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T20:44:12Z","timestamp":1640292252000},"page":"1255-1268","source":"Crossref","is-referenced-by-count":29,"title":["Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment"],"prefix":"10.1109","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8619-236X","authenticated-orcid":false,"given":"Weihang","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-8083","authenticated-orcid":false,"given":"Xiaomeng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan Hang Keith","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael D.","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3885-4912","authenticated-orcid":false,"given":"Kwang-Ting","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2951844"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102031"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2806309"},{"key":"ref5","first-page":"46","article-title":"Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI","volume":"13","author":"Kamnitsas","year":"2015","journal-title":"Ischemic Stroke Lesion Segmentation"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/i.media.2016.10.004"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995319"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2147\/RRFMS.S164933"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2008.12.007"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-007-0479-9"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2011.02.002"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2015.7350983"},{"key":"ref14","first-page":"1","article-title":"Learning depth from single monocular images","volume-title":"Proc. NeurIPS","volume":"18","author":"Saxena"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2762762"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2145-8"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87240-3_4"},{"key":"ref18","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25779-x"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_35"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451312"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.324"},{"key":"ref24","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. ICML","author":"Chen"},{"key":"ref25","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","author":"Caron","year":"2020","journal-title":"arXiv:2006.09882"},{"key":"ref26","article-title":"Prototypical contrastive learning of unsupervised representations","author":"Li","year":"2020","journal-title":"arXiv:2005.04966"},{"key":"ref27","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04297"},{"key":"ref28","article-title":"Supervised contrastive learning","author":"Khosla","year":"2020","journal-title":"arXiv:2004.11362"},{"key":"ref29","article-title":"TCLR: Temporal contrastive learning for video representation","author":"Dave","year":"2021","journal-title":"arXiv:2101.07974"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_6"},{"issue":"2","key":"ref32","first-page":"3","article-title":"Mid-level visual element discovery as discriminative mode seeking","volume-title":"Proc. NeurIPS","volume":"1","author":"Doersch"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.559"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref36","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013","journal-title":"arXiv:1312.6114"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref38","article-title":"Unsupervised representation learning by predicting image rotations","author":"Gidaris","year":"2018","journal-title":"arXiv:1803.07728"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00265"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref42","article-title":"What makes for good views for contrastive learning?","author":"Tian","year":"2020","journal-title":"arXiv:2005.10243"},{"key":"ref43","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. NeurIPS","volume":"33","author":"You"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00304"},{"key":"ref46","article-title":"Contrastive learning for label-efficient semantic segmentation","author":"Zhao","year":"2020","journal-title":"arXiv:2012.06985"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"issue":"2","key":"ref48","first-page":"207","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref50","first-page":"1857","article-title":"Improved deep metric learning with multi-class n-pair loss objective","volume-title":"Proc. NeurIPS","author":"Sohn"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_5"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00393"},{"key":"ref53","article-title":"A new window loss function for bone fracture detection and localization in X-ray images with point-based annotation","author":"Zhang","year":"2020","journal-title":"arXiv:2012.04066"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190023"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2019.106530"},{"key":"ref56","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-76193\/v1","volume-title":"Radiographic bone texture analysis using deep learning models for early rheumatoid arthritis diagnosis","author":"Huang","year":"2020"},{"key":"ref57","first-page":"403","article-title":"Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning","volume-title":"Proc. MLHC","author":"Lu"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-74113-0_10"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.10090232"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8512755"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87602-9_13"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2007.11.061"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2018.1456"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10470-6_73"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433781"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101873"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87231-1_48"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511802256"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1137\/1036146"},{"key":"ref70","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. ICML","author":"Tan"},{"key":"ref71","article-title":"Augment your batch: Better training with larger batches","author":"Hoffer","year":"2019","journal-title":"arXiv:1901.09335"},{"key":"ref72","first-page":"1","article-title":"EchoNet-Dynamic: A large new cardiac motion video data resource for medical machine learning","volume-title":"Proc. NeurIPS","author":"Ouyang"},{"key":"ref73","article-title":"The kinetics human action video dataset","author":"Kay","year":"2017","journal-title":"arXiv:1705.06950"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2900516"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/9766219\/09661384.pdf?arnumber=9661384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:32:21Z","timestamp":1704846741000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9661384\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5]]},"references-count":74,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2021.3137854","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5]]}}}