{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:27:23Z","timestamp":1773264443877,"version":"3.50.1"},"reference-count":16,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1016\/j.ejrad.2022.110369","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T11:34:18Z","timestamp":1653305658000},"page":"110369","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"special_numbering":"C","title":["Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model"],"prefix":"10.1016","volume":"154","author":[{"given":"Jang-Hoon","family":"Oh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyug-Gi","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyung Mi","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-Woo","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ejrad.2022.110369_b0005","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1148\/radiol.2019182716","article-title":"A deep learning mammography-based model for improved breast cancer risk prediction","volume":"292","author":"Yala","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2022.110369_b0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-48995-4","article-title":"Deep learning to improve breast cancer detection on screening mammography","volume":"9","author":"Shen","year":"2019","journal-title":"Sci. Rep."},{"issue":"23","key":"10.1016\/j.ejrad.2022.110369_b0015","doi-asserted-by":"crossref","first-page":"5902","DOI":"10.1158\/1078-0432.CCR-18-1115","article-title":"Deep learning to distinguish recalled but benign mammography images in breast cancer screening","volume":"24","author":"Aboutalib","year":"2018","journal-title":"Clin. Cancer Res."},{"key":"10.1016\/j.ejrad.2022.110369_b0020","first-page":"1","article-title":"Discrimination of breast cancer with microcalcifications on mammography by deep learning","volume":"6","author":"Wang","year":"2016","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2022.110369_b0025","doi-asserted-by":"crossref","first-page":"386","DOI":"10.12659\/PJR.897304","article-title":"Evaluation of Doses and Image Quality in Mammography with Screen-Film, CR, and DR Detectors - Application of the ACR Phantom","volume":"81","author":"\u015alusarczyk-Kacprzyk","year":"2016","journal-title":"Pol J Radiol"},{"issue":"7","key":"10.1016\/j.ejrad.2022.110369_b0030","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/S1076-6332(03)80345-8","article-title":"How good is the ACR accreditation phantom for assessing image quality in digital mammography?","volume":"9","author":"Huda","year":"2002","journal-title":"Acad Radiol"},{"issue":"15","key":"10.1016\/j.ejrad.2022.110369_b0035","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1001\/jama.2015.13183","article-title":"Benefits and Harms of Breast Cancer Screening: a Systematic Review","volume":"314","author":"Myers","year":"2015","journal-title":"JAMA"},{"issue":"7","key":"10.1016\/j.ejrad.2022.110369_b0040","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1002\/ijc.30794","article-title":"Benefits and harms of breast cancer screening with mammography in women aged 40\u201349 years: a systematic review","volume":"141","author":"van den Ende","year":"2017","journal-title":"Int J Cancer"},{"issue":"1","key":"10.1016\/j.ejrad.2022.110369_b0045","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1097\/00004424-199401000-00006","article-title":"Subjective evaluations of mammographic accreditation phantom images by three observer groups","volume":"29","author":"Brooks","year":"1994","journal-title":"Invest. Radiol."},{"issue":"9","key":"10.1016\/j.ejrad.2022.110369_b0050","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1038\/s41591-020-1041-y","article-title":"Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist","volume":"26","author":"Norgeot","year":"2020","journal-title":"Nat. Med."},{"key":"10.1016\/j.ejrad.2022.110369_b0055","series-title":"2018 Digital Mammography QUALITY CONTROL MANUAL","author":"Eric","year":"2018"},{"key":"10.1016\/j.ejrad.2022.110369_b0060","series-title":"In: Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7263","article-title":"YOLO9000: better, faster, stronger","author":"Redmon","year":"2017"},{"key":"10.1016\/j.ejrad.2022.110369_b0065","series-title":"In: Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"5","key":"10.1016\/j.ejrad.2022.110369_b0070","doi-asserted-by":"crossref","first-page":"e232","DOI":"10.1016\/S2589-7500(19)30108-6","article-title":"Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study","volume":"1","author":"Faes","year":"2019","journal-title":"The Lancet Digital Health"},{"issue":"8","key":"10.1016\/j.ejrad.2022.110369_b0075","doi-asserted-by":"crossref","first-page":"100129","DOI":"10.1016\/j.patter.2020.100129","article-title":"Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability","volume":"1","author":"Ho","year":"2020","journal-title":"Patterns (N Y)"},{"issue":"6","key":"10.1016\/j.ejrad.2022.110369_b0080","doi-asserted-by":"crossref","first-page":"942","DOI":"10.21037\/qims.2019.05.15","article-title":"Improvement diagnostic accuracy of sinusitis recognition in paranasal sinus X-ray using multiple deep learning models","volume":"9","author":"Kim","year":"2019","journal-title":"Quant Imaging Med Surg"}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X22002194?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X22002194?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T07:29:49Z","timestamp":1759994989000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X22002194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":16,"alternative-id":["S0720048X22002194"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2022.110369","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2022.110369","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"110369"}}