{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T20:45:31Z","timestamp":1793479531878,"version":"4.5.0"},"reference-count":26,"publisher":"American Medical Association (AMA)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"DOI":"10.1001\/jamanetworkopen.2021.19100","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T15:34:51Z","timestamp":1629128091000},"page":"e2119100","source":"Crossref","is-referenced-by-count":99,"title":["A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images"],"prefix":"10.1001","volume":"4","author":[{"given":"Mateusz","family":"Buda","sequence":"first","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashirbani","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Walsh","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujata","family":"Ghate","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nianyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Swiecicki","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph Y.","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"},{"name":"Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maciej A.","family":"Mazurowski","sequence":"additional","affiliation":[{"name":"Department of Radiology, Duke University Medical Center, Durham, North Carolina"},{"name":"Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina"},{"name":"Department of Computer Science, Duke University, Durham, North Carolina"},{"name":"Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"zoi210568r2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis.","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"issue":"5","key":"zoi210568r3","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.crad.2019.02.006","article-title":"Artificial intelligence in breast imaging.","volume":"74","author":"Le","year":"2019","journal-title":"Clin Radiol"},{"issue":"3","key":"zoi210568r4","doi-asserted-by":"publisher","first-page":"e200265","DOI":"10.1001\/jamanetworkopen.2020.0265","article-title":"Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms.","volume":"3","author":"Schaffter","year":"2020","journal-title":"JAMA Netw Open"},{"issue":"3","key":"zoi210568r5","doi-asserted-by":"publisher","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit Health"},{"issue":"7788","key":"zoi210568r6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening.","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"issue":"2","key":"zoi210568r7","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1148\/radiol.2019182627","article-title":"Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives.","volume":"293","author":"Geras","year":"2019","journal-title":"Radiology"},{"issue":"3","key":"zoi210568r8","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1002\/mp.2018.45.issue-3","article-title":"Deep learning for segmentation of brain tumors: impact of cross-institutional training and testing.","volume":"45","author":"AlBadawy","year":"2018","journal-title":"Med Phys"},{"issue":"3","key":"zoi210568r9","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1148\/radiol.2015141303","article-title":"Digital breast tomosynthesis: state of the art.","volume":"277","author":"Vedantham","year":"2015","journal-title":"Radiology"},{"key":"zoi210568r18","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks.","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw"},{"issue":"12","key":"zoi210568r22","doi-asserted-by":"publisher","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":"3","key":"zoi210568r23","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab98d0","article-title":"Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis.","volume":"66","author":"Li","year":"2021","journal-title":"Phys Med Biol"},{"key":"zoi210568r24","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2020.599333","article-title":"Mass detection and segmentation in digital breast tomosynthesis using 3D-mask region-based convolutional neural network: a comparative analysis.","volume":"7","author":"Fan","year":"2020","journal-title":"Front Mol Biosci"},{"issue":"6","key":"zoi210568r25","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.acra.2018.06.019","article-title":"Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography.","volume":"26","author":"Mendel","year":"2019","journal-title":"Acad Radiol"},{"issue":"2","key":"zoi210568r26","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/s41591-020-01174-9","article-title":"Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.","volume":"27","author":"Lotter","year":"2021","journal-title":"Nat Med"},{"key":"zoi210568r11","author":"Redmon","year":"2016"},{"key":"zoi210568r12","author":"Huang","year":"2017"},{"key":"zoi210568r15","author":"He","year":"2015"},{"key":"zoi210568r17","author":"Neubeck"},{"key":"zoi210568r1","unstructured":"Krizhevsky? A, Sutskever? I, Hinton? GE. Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, eds. Advances in Neural Information Processing Systems 25. Accessed July 7, 2021. https:\/\/papers.nips.cc\/paper\/2012\/file\/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf"},{"key":"zoi210568r10","unstructured":"Breast cancer screening\u2014digital breast tomosynthesis (BCS-DBT). Cancer Imaging Archive. June 9, 2021. Accessed July 14, 2021. https:\/\/wiki.cancerimagingarchive.net\/pages\/viewpage.action?pageId=64685580"},{"key":"zoi210568r13","unstructured":"Ren? S, He? K, Girshick? R, Sun? J. Faster R-CNN: towards real-time object detection with region proposal networks. Accessed July 7, 2021. https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/14bfa6bb14875e45bba028a21ed38046-Paper.pdf"},{"key":"zoi210568r14","unstructured":"Kingma? DP, Ba? J. Adam: a method for stochastic optimization. arXiv. Preprint updated January 30, 2017. Accessed July 7, 2021. https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"zoi210568r16","unstructured":"Lin? T-Y, Goyal? P, Girshick? R, He? K, Doll\u00e1r? P. Focal loss for dense object detection. arXiv. Preprint updated February 7, 2018. Accessed July 7, 2021. https:\/\/arxiv.org\/abs\/1708.02002"},{"key":"zoi210568r19","unstructured":"Sergievskiy? N, Ponamarev? A. Reduced focal loss: 1st place solution to xView object detection in satellite imagery. arXiv. Preprint updated April 25, 2019. Accessed July 7, 2021. https:\/\/arxiv.org\/abs\/1903.01347"},{"key":"zoi210568r20","unstructured":"Duke DBT data. Github. Accessed July 14, 2021. https:\/\/github.com\/MaciejMazurowski\/duke-dbt-data"},{"key":"zoi210568r21","unstructured":"Machine learning statistical utils. Github. Accessed July 14, 2021. https:\/\/github.com\/mateuszbuda\/ml-stat-util"}],"container-title":["JAMA Network Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/articlepdf\/2783046\/buda_2021_oi_210568_1628278929.17056.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T15:34:54Z","timestamp":1629128094000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2783046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,16]]},"references-count":26,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8,2]]}},"URL":"https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100","relation":{},"ISSN":["2574-3805"],"issn-type":[{"value":"2574-3805","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,16]]}}}