{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T16:28:33Z","timestamp":1790872113763,"version":"4.1.0"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"am","delay-in-days":377,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.acra.2022.12.038","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:48:25Z","timestamp":1673311705000},"page":"S161-S171","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":78,"special_numbering":"S2","title":["Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification"],"prefix":"10.1016","volume":"30","author":[{"given":"Yang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Lin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiejie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeon-Hor","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bomi","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ritesh","family":"Parajuli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rita S.","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meihao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3069-0271","authenticated-orcid":false,"given":"Min-Ying","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.acra.2022.12.038_bib0001","first-page":"7","article-title":"Cancer statistics, 2021","volume":"71","author":"Siegel","year":"2021","journal-title":"CA Cancer J Clin"},{"issue":"15","key":"10.1016\/j.acra.2022.12.038_bib0002","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1001\/jama.2015.12783","article-title":"Breast cancer screening for women at average risk: 2015 Guideline Update From the American Cancer Society","volume":"314","author":"Oeffinger","year":"2015","journal-title":"JAMA"},{"issue":"2","key":"10.1016\/j.acra.2022.12.038_bib0003","first-page":"75","article-title":"American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography","volume":"57","author":"Saslow","year":"2007","journal-title":"CA Cancer J Clin"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0004","doi-asserted-by":"crossref","first-page":"889","DOI":"10.2214\/AJR.13.12264","article-title":"Breast MRI as an adjunct to mammography for breast cancer screening in high-risk patients: retrospective review","volume":"204","author":"Raikhlin","year":"2015","journal-title":"AJR Am J Roentgenol"},{"issue":"2","key":"10.1016\/j.acra.2022.12.038_bib0005","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1002\/jmri.25790","article-title":"Multiparametric MRI of the breast: A review","volume":"47","author":"Marino","year":"2018","journal-title":"J Magn Reson Imaging"},{"issue":"2","key":"10.1016\/j.acra.2022.12.038_bib0006","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1002\/jmri.26654","article-title":"Contrast-enhanced MRI for breast cancer screening","volume":"50","author":"Mann","year":"2019","journal-title":"J Magn Reson Imaging"},{"issue":"9","key":"10.1016\/j.acra.2022.12.038_bib0007","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1118\/1.598345","article-title":"Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging","volume":"25","author":"Gilhuijs","year":"1998","journal-title":"Med Phys"},{"issue":"12","key":"10.1016\/j.acra.2022.12.038_bib0008","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.acra.2008.06.005","article-title":"Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI","volume":"15","author":"Nie","year":"2008","journal-title":"Acad Radiol"},{"issue":"8","key":"10.1016\/j.acra.2022.12.038_bib0009","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1007\/s00330-014-3166-1","article-title":"Computer-aided evaluation as an adjunct to revised BI-RADS Atlas: improvement in positive predictive value at screening breast MRI","volume":"24","author":"Gweon","year":"2014","journal-title":"Eur Radiol"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0010","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s00330-009-1616-y","article-title":"Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement","volume":"20","author":"Newell","year":"2010","journal-title":"Eur Radiol"},{"issue":"7","key":"10.1016\/j.acra.2022.12.038_bib0011","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1016\/j.ejrad.2011.03.060","article-title":"Contralateral lesions detected by preoperative MRI in patients with recently diagnosed breast cancer: application of MR CAD in differentiation of benign and malignant lesions","volume":"81","author":"Cho","year":"2012","journal-title":"Eur J Radiol"},{"issue":"8","key":"10.1016\/j.acra.2022.12.038_bib0012","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.crad.2018.04.014","article-title":"The value of breast MRI for BI-RADS category 4B mammographic microcalcification: based on the 5th edition of BI-RADS","volume":"73","author":"Eun","year":"2018","journal-title":"Clin Radiol"},{"issue":"3","key":"10.1016\/j.acra.2022.12.038_bib0013","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1148\/radiol.2015150241","article-title":"Improving the accuracy of computer-aided diagnosis for breast mr imaging by differentiating between mass and nonmass lesions","volume":"278","author":"Gallego-Ortiz","year":"2016","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0014","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"issue":"2","key":"10.1016\/j.acra.2022.12.038_bib0015","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.acra.2022.12.038_bib0016","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1148\/radiol.2018181352","article-title":"Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI","volume":"290","author":"Truhn","year":"2019","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.acra.2022.12.038_bib0017","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1002\/jmri.26981","article-title":"Diagnosis of benign and malignant breast lesions on DCE-MRI by using radiomics and deep learning with consideration of peritumor tissue","volume":"51","author":"Zhou","year":"2020","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0018","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.1007\/s00330-020-07274-x","article-title":"Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers","volume":"31","author":"Zhang","year":"2021","journal-title":"Eur Radiol"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0019","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: general overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.acra.2022.12.038_bib0020","first-page":"1230","article-title":"Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network","volume":"2017","author":"Al-Masni","year":"2017","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"2","key":"10.1016\/j.acra.2022.12.038_bib0021","doi-asserted-by":"crossref","first-page":"280","DOI":"10.2214\/AJR.18.20389","article-title":"Artificial intelligence for breast MRI in 2008-2018: a systematic mapping review","volume":"212","author":"Codari","year":"2019","journal-title":"AJR Am J Roentgenol"},{"issue":"5","key":"10.1016\/j.acra.2022.12.038_bib0022","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1002\/jmri.26878","article-title":"Artificial intelligence in the interpretation of breast cancer on MRI","volume":"51","author":"Sheth","year":"2020","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"10.1016\/j.acra.2022.12.038_bib0023","article-title":"Fully automated detection of breast cancer in screening MRI using convolutional neural networks","volume":"5","author":"Dalm\u0131\u015f","year":"2018","journal-title":"J Med Imaging (Bellingham)"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0024","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","article-title":"Automated breast ultrasound lesions detection using convolutional neural networks","volume":"22","author":"Yap","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.acra.2022.12.038_bib0025","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med Image Anal"},{"issue":"12","key":"10.1016\/j.acra.2022.12.038_bib0026","doi-asserted-by":"crossref","first-page":"6654","DOI":"10.1118\/1.4967345","article-title":"Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography","volume":"43","author":"Samala","year":"2016","journal-title":"Med Phys"},{"issue":"1","key":"10.1016\/j.acra.2022.12.038_bib0027","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1038\/s41598-018-22437-z","article-title":"Detecting and classifying lesions in mammograms with Deep Learning","volume":"8","author":"Ribli","year":"2018","journal-title":"Sci Rep"},{"issue":"Suppl 1","key":"10.1016\/j.acra.2022.12.038_bib0028","doi-asserted-by":"crossref","first-page":"S135","DOI":"10.1016\/j.acra.2020.12.001","article-title":"Automatic detection and segmentation of breast cancer on MRI Using Mask R-CNN trained on non-fat-sat images and tested on fat-sat images","volume":"29","author":"Zhang","year":"2022","journal-title":"Acad Radiol"},{"issue":"22","key":"10.1016\/j.acra.2022.12.038_bib0029","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Ehteshami Bejnordi","year":"2017","journal-title":"JAMA"},{"issue":"1","key":"10.1016\/j.acra.2022.12.038_bib0030","doi-asserted-by":"crossref","first-page":"2762","DOI":"10.1038\/s41598-018-21215-1","article-title":"Applying data-driven imaging biomarker in mammography for breast cancer screening: preliminary study","volume":"8","author":"Kim","year":"2018","journal-title":"Sci Rep"},{"key":"10.1016\/j.acra.2022.12.038_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105812","article-title":"SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection","volume":"148","author":"Lu","year":"2022","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.acra.2022.12.038_bib0032","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compmedimag.2018.09.004","article-title":"SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis","volume":"70","author":"Gao","year":"2018","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0033","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.diii.2019.02.008","article-title":"Detection and characterization of MRI breast lesions using deep learning","volume":"100","author":"Herent","year":"2019","journal-title":"Diagn Interv Imaging"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0034","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1002\/jmri.26721","article-title":"Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images","volume":"50","author":"Zhou","year":"2019","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2022.12.038_bib0035","doi-asserted-by":"crossref","DOI":"10.1007\/s00330-022-08863-8","article-title":"Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time","author":"Jing","year":"2022","journal-title":"Eur Radiol"},{"issue":"10","key":"10.1016\/j.acra.2022.12.038_bib0036","doi-asserted-by":"crossref","first-page":"5897","DOI":"10.1002\/mp.15156","article-title":"Automatic breast lesion detection in ultrafast DCE-MRI using deep learning","volume":"48","author":"Ayatollahi","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2022.12.038_bib0037","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.728224","article-title":"BI-RADS reading of non-mass lesions on dce-mri and differential diagnosis performed by radiomics and deep learning","volume":"11","author":"Zhou","year":"2021","journal-title":"Front Oncol"},{"issue":"4","key":"10.1016\/j.acra.2022.12.038_bib0038","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1002\/jmri.21915","article-title":"Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis","volume":"30","author":"Lin","year":"2009","journal-title":"J Magn Reson Imaging"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S107663322200695X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S107663322200695X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T08:57:38Z","timestamp":1761037058000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S107663322200695X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":38,"alternative-id":["S107663322200695X"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2022.12.038","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2022.12.038","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}