A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to t...

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Bibliographic Details
Published in:	Proceedings of the IEEE Vol. 109; no. 5; pp. 820 - 838
Main Authors:	Zhou, S. Kevin, Greenspan, Hayit, Davatzikos, Christos, Duncan, James S., Van Ginneken, Bram, Madabhushi, Anant, Prince, Jerry L., Rueckert, Daniel, Summers, Ronald M.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Annotations Artificial intelligence Biomedical imaging Case studies Clinical diagnosis Computed tomography Computer architecture Deep learning Deep learning (DL) Digital imaging Diseases Image segmentation Literature reviews Medical diagnostic imaging Medical imaging Medical services Network architecture survey Trends deep learning survey Medical imaging
ISSN:	0018-9219, 1558-2256
Online Access:	Get full text
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Abstract	Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
AbstractList	Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high-performance computing. However, medical imaging presents unique challenges that confront DL approaches. In this survey article, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in DL are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, and so on. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions. Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions. Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
Author	Davatzikos, Christos Madabhushi, Anant Greenspan, Hayit Duncan, James S. Van Ginneken, Bram Summers, Ronald M. Zhou, S. Kevin Prince, Jerry L. Rueckert, Daniel
Author_xml	– sequence: 1 givenname: S. Kevin orcidid: 0000-0002-6881-4444 surname: Zhou fullname: Zhou, S. Kevin email: zhoushaohua@ict.ac.cn organization: School of Biomedical Engineering, University of Science and Technology of China, Hefei, China – sequence: 2 givenname: Hayit surname: Greenspan fullname: Greenspan, Hayit organization: Department of Biomedical Engineering, Faculty of Engineering, Tel-Aviv University, Tel-Aviv, Israel – sequence: 3 givenname: Christos surname: Davatzikos fullname: Davatzikos, Christos organization: Radiology Department, University of Pennsylvania, Philadelphia, PA, USA – sequence: 4 givenname: James S. orcidid: 0000-0002-5167-9856 surname: Duncan fullname: Duncan, James S. organization: Department of Biomedical Engineering, Yale University, New Haven, CT, USA – sequence: 5 givenname: Bram surname: Van Ginneken fullname: Van Ginneken, Bram organization: Radboud University Medical Center, Nijmegen, GA, The Netherlands – sequence: 6 givenname: Anant orcidid: 0000-0002-5741-0399 surname: Madabhushi fullname: Madabhushi, Anant organization: Department of Biomedical Engineering, CaseWestern Reserve University, Cleveland, OH, USA – sequence: 7 givenname: Jerry L. orcidid: 0000-0002-6553-0876 surname: Prince fullname: Prince, Jerry L. organization: Electrical and Computer Engineering Department, Johns Hopkins University, Baltimore, MD, USA – sequence: 8 givenname: Daniel orcidid: 0000-0002-5683-5889 surname: Rueckert fullname: Rueckert, Daniel organization: Klinikum rechts der Isar, Technical University of Munich (TU Munich), Munich, Germany – sequence: 9 givenname: Ronald M. orcidid: 0000-0001-8081-7376 surname: Summers fullname: Summers, Ronald M. organization: National Institutes of Health Clinical Center, Bethesda, MD, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37786449$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.nicl.2018.08.019 10.1007/978-3-030-20351-1_34 10.1007/978-3-030-32254-0_54 10.1109/TMI.2016.2521800 10.1148/ryct.2020190032 10.1038/s41591-018-0177-5 10.1016/j.media.2019.01.010 10.1007/s00261-017-1294-1 10.1109/CVPR.2017.369 10.1016/j.radonc.2018.10.019 10.1371/journal.pcbi.1006376 10.1148/rg.316105207 10.1148/rg.341135036 10.1007/978-3-030-35817-4_8 10.1109/ISBI.2017.7950500 10.1007/978-3-030-00931-1_24 10.1016/j.celrep.2018.03.086 10.1016/j.media.2017.07.005 10.1007/978-3-030-59719-1_41 10.1016/j.media.2019.101552 10.1016/j.media.2017.10.005 10.1109/TMI.2018.2866845 10.1109/TMI.2020.2995108 10.1007/978-3-030-32245-8_29 10.1109/3DV.2016.79 10.1016/j.media.2018.10.005 10.1177/0846537120949974 10.1109/TMI.2019.2943841 10.1016/j.bpsc.2019.11.007 10.1007/978-3-030-00934-2_79 10.1007/978-3-030-32245-8_14 10.1109/ACCESS.2017.2788044 10.1093/brain/awaa160 10.1016/j.media.2019.02.009 10.1016/j.ebiom.2019.08.023 10.1007/978-3-319-68127-6_2 10.1001/jama.2016.17216 10.1007/978-3-030-00946-5_16 10.1109/ACCESS.2019.2899635 10.1109/3DV.2019.00035 10.1016/j.media.2019.03.009 10.1016/j.media.2019.04.002 10.1007/s10278-019-00207-1 10.1007/978-3-319-66179-7_58 10.1038/srep46450 10.1109/TMI.2018.2858202 10.1038/s41598-020-62148-y 10.1016/j.media.2020.101800 10.1038/s41591-019-0508-1 10.1148/radiol.2018180940 10.1109/RBME.2020.2987975 10.1016/j.media.2013.10.012 10.1016/j.neuroimage.2017.07.059 10.1002/mp.13764 10.1109/ICCV.2017.81 10.1109/TMI.2015.2458702 10.4103/2153-3539.186902 10.1038/s41591-019-0583-3 10.18653/v1/P18-1240 10.1016/j.media.2020.101765 10.1016/j.media.2020.101715 10.1186/s12968-018-0471-x 10.1117/1.JMI.5.4.044501 10.1016/j.media.2020.101724 10.1109/TMI.2017.2743464 10.1109/TMI.2019.2933425 10.1016/j.media.2014.01.010 10.1007/978-3-030-59713-9_5 10.1109/JPROC.2019.2943836 10.24963/ijcai.2018/96 10.1016/S1470-2045(19)30739-9 10.1109/ISBI.2015.7163871 10.1371/journal.pone.0196828 10.1007/978-3-030-59725-2_45 10.3389/fcvm.2020.00017 10.1109/TMI.2020.2986242 10.1007/978-3-030-32226-7_31 10.1016/j.media.2019.04.007 10.1109/CVPR.2017.502 10.1016/j.acra.2018.07.006 10.1016/j.media.2019.101535 10.1109/TCI.2020.2964229 10.1007/978-3-030-59719-1_21 10.1016/j.neuroimage.2017.04.039 10.1016/j.neuroimage.2018.08.003 10.1016/S2589-7500(20)30025-X 10.1007/978-3-030-59719-1_56 10.1016/0090-4295(85)90280-8 10.1109/TMI.2019.2897538 10.1007/978-3-030-32245-8_6 10.1007/978-3-319-24553-9_83 10.1109/TMI.2019.2905990 10.1001/jama.2017.14585 10.1109/CVPR.2015.7298594 10.1016/j.neuroimage.2016.01.024 10.1117/1.JMI.5.3.036501 10.1146/annurev-bioeng-071516-044442 10.1109/TMI.2015.2482920 10.1088/2057-1976/aad100 10.1016/S0168-8278(20)31254-X 10.1007/978-3-030-20351-1_19 10.1002/mp.12399 10.1038/s41467-019-13163-9 10.1109/CVPR.2019.01076 10.1016/j.media.2019.101557 10.1007/978-3-030-00928-1_12 10.1007/978-3-319-66185-8_29 10.1148/radiol.2020201473 10.1016/j.jacr.2019.05.034 10.1038/srep46479 10.1109/TMI.2019.2948320 10.3389/fcvm.2019.00195 10.1007/978-3-030-00937-3_43 10.1016/j.media.2018.01.006 10.1016/j.media.2017.12.009 10.1609/aaai.v33i01.3301590 10.1109/CVPR.2016.90 10.1007/s00330-020-06672-5 10.1109/CVPR.2018.00745 10.1016/j.media.2019.04.014 10.1016/j.media.2019.101619 10.1007/s00138-020-01060-x 10.1016/j.crad.2014.09.017 10.1016/j.neucom.2018.09.013 10.1038/s41746-019-0104-2 10.1109/EMBC.2019.8857465 10.1016/j.media.2020.101693 10.1002/jmri.26907 10.1016/j.neuroimage.2017.04.041 10.1109/CVPR.2018.00963 10.1007/978-3-030-32226-7_75 10.1038/s41598-020-61055-6 10.1007/978-3-030-00937-3_67 10.1016/j.neuroimage.2017.07.008 10.1007/s10278-019-00227-x 10.1109/TMI.2016.2553401 10.1016/j.media.2016.05.004 10.1148/ryai.2020190102 10.1016/j.media.2020.101746 10.1109/TMI.2020.2994459 10.1109/CVPR.2017.243 10.1109/CVPR.2010.5540016 10.1016/S1076-6332(03)00671-8 10.1093/jnci/djx137 10.1016/j.compmedimag.2018.03.001 10.1016/j.neuroimage.2017.03.057 10.1158/1078-0432.CCR-18-2013 10.1001/jamanetworkopen.2019.14645 10.1148/radiol.2020201874 10.1007/978-3-030-32251-9_72 10.1007/978-3-319-59050-9_47 10.1109/ISBI.2018.8363679 10.1016/j.media.2019.06.001 10.1148/rg.2018170173 10.1038/srep32706 10.1007/s00432-019-03098-5 10.1038/s41591-019-0447-x 10.1016/j.media.2016.10.004 10.1016/S1470-2045(19)30793-4 10.1038/s42256-019-0019-2 10.1007/978-3-030-00934-2_94 10.1117/1.JMI.6.2.024007 10.1016/S0140-6736(19)32998-8 10.1038/s41591-019-0462-y 10.1109/TPAMI.2016.2572683 10.1007/978-3-030-00934-2_52 10.1109/ISBI.2019.8759212 10.1007/978-3-030-32251-9_42 10.1016/j.acra.2012.05.013 10.1016/j.neuroimage.2020.116819 10.1186/s40708-018-0080-3 10.1145/3298981 10.1109/ISBI.2018.8363518 10.1007/978-3-030-32692-0_16 10.1002/cyto.a.23065 10.1109/TMI.2016.2528162 10.1073/pnas.1900654116 10.1016/j.neuroimage.2019.03.041 10.1016/j.compmedimag.2019.101684 10.1016/j.mri.2019.05.041 10.1109/JPROC.2019.2956422 10.1038/nature14539 10.1016/j.neuroimage.2017.02.035 10.1088/978-0-7503-2216-4 10.1007/s10278-018-0150-3 10.1016/j.ebiom.2018.03.017 10.1088/1361-6560/ab843e 10.1038/s41571-019-0252-y 10.1016/S1470-2045(19)30738-7 10.1007/978-3-030-05831-9_16 10.1007/978-3-030-00934-2_53 10.1016/j.neuroimage.2012.01.021 10.1007/978-3-030-00937-3_71 10.1007/978-3-319-46723-8_55 10.1148/radiol.10092437 10.3109/15412550903499522 10.1109/CVPR.2018.00716 10.1148/radiol.2019190512
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References	ref57 ref207 ref208 ref59 ref205 ref58 ref206 ref53 ref203 ref204 ref55 ref202 ref209 ref210 ref211 ref51 beutel (ref8) 2000; 2 isensee (ref119) 2019 ref218 ref45 ref219 ref216 ref47 ref217 ref42 ref214 bustos (ref110) 2019 tamada (ref147) 2018 ref41 ref215 oktay (ref49) 2018 ref212 ref213 ref7 ref4 ref3 bagdasaryan (ref83) 2020 ref5 ref100 ref221 ref222 ref40 ref220 campello (ref174) 2020 beutel (ref1) 2000; 1 ref35 ref34 ref37 ref36 ref31 ref30 johnson (ref109) 2019 ref23 ref26 ref25 ref20 ref22 liu (ref139) 2014 xu (ref152) 2017 li (ref133) 2017 howard (ref52) 2017 ref28 ref27 ref29 roth (ref201) 2015 börgers (ref6) 2012; 110 ref200 (ref32) 2013 roth (ref194) 2015 yang (ref155) 2020 ref128 ref129 ref97 ref126 ref247 ref96 ref127 ref99 ref124 ref245 ref98 ref125 ref246 lee (ref43) 2015 yao (ref235) 2020 simpson (ref185) 2019 ref93 ref92 ref134 ref95 ref131 ref132 ref91 ref90 ref89 vu tran (ref164) 2016 ref86 ref137 ref85 ref138 tang (ref190) 2020 ref88 ref135 ref87 ref136 araújo (ref101) 2020; 63 ref144 kone?ný (ref81) 2016 ref145 goodfellow (ref44) 2014 ref142 ref143 ref141 ref80 gozes (ref241) 2020 ref79 ref229 ref108 ref78 ref227 ref106 ref228 ref107 ref75 ref225 ref104 ref74 ref226 ref105 ref77 ref223 ref102 ref76 ref224 ref103 prince (ref9) 2006 ref71 ref232 ref70 ref233 ref112 ref73 ref230 ref72 ref231 ref67 ref238 ref69 ref239 ref118 ref64 ref236 (ref111) 2019 ref115 ref63 ref237 ref116 ref66 ref234 ref113 ref65 ref114 isensee (ref166) 2017 ref243 ref122 ref244 ref123 ref62 ref120 ref61 ref242 ref121 ref240 zhou (ref11) 2019 ref168 ref169 ref170 ref177 ref178 ref175 gal (ref94) 2016 ref176 ref171 ref172 ref179 krizhevsky (ref33) 2012 ref180 ref181 ref188 ref189 ref186 ref187 ref184 ref182 ref183 ref148 ref149 ref146 simonyan (ref39) 2014 zhang (ref48) 2019 sheller (ref84) 2018 ref156 yang (ref173) 2019 ref153 ref154 ref151 ref150 ref159 ref157 ref158 lu (ref140) 2018; 8 lecun (ref21) 2015; 521 ref165 ref162 ref163 ref160 ref161 elsken (ref50) 2018 ref13 ref12 ref15 ref14 ref17 raghu (ref54) 2019 ref16 ref19 ref18 xu (ref46) 2015 wolterink (ref167) 2017 shi (ref68) 2020 murphy (ref117) 2020; 296 zhou (ref24) 2017 ref2 bakas (ref130) 2018 ref191 ref192 chen (ref56) 2019 huang (ref60) 2019 ref199 ref197 ref198 ref195 ref196 ref193 bankman (ref10) 2008 zhao (ref82) 2018 ronneberger (ref38) 2015
References_xml	– ident: ref245 doi: 10.1016/j.nicl.2018.08.019 – ident: ref180 doi: 10.1007/978-3-030-20351-1_34 – ident: ref90 doi: 10.1007/978-3-030-32254-0_54 – ident: ref134 doi: 10.1109/TMI.2016.2521800 – ident: ref176 doi: 10.1148/ryct.2020190032 – ident: ref219 doi: 10.1038/s41591-018-0177-5 – ident: ref69 doi: 10.1016/j.media.2019.01.010 – ident: ref206 doi: 10.1007/s00261-017-1294-1 – ident: ref65 doi: 10.1109/CVPR.2017.369 – ident: ref208 doi: 10.1016/j.radonc.2018.10.019 – ident: ref145 doi: 10.1371/journal.pcbi.1006376 – ident: ref4 doi: 10.1148/rg.316105207 – year: 2020 ident: ref155 article-title: Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images publication-title: arXiv 2006 15255 – ident: ref2 doi: 10.1148/rg.341135036 – ident: ref184 doi: 10.1007/978-3-030-35817-4_8 – ident: ref149 doi: 10.1109/ISBI.2017.7950500 – ident: ref91 doi: 10.1007/978-3-030-00931-1_24 – ident: ref233 doi: 10.1016/j.celrep.2018.03.086 – ident: ref23 doi: 10.1016/j.media.2017.07.005 – ident: ref211 doi: 10.1007/978-3-030-59719-1_41 – ident: ref27 doi: 10.1016/j.media.2019.101552 – ident: ref142 doi: 10.1016/j.media.2017.10.005 – ident: ref170 doi: 10.1109/TMI.2018.2866845 – ident: ref105 doi: 10.1109/TMI.2020.2995108 – ident: ref74 doi: 10.1007/978-3-030-32245-8_29 – ident: ref120 doi: 10.1109/3DV.2016.79 – ident: ref181 doi: 10.1016/j.media.2018.10.005 – ident: ref7 doi: 10.1177/0846537120949974 – ident: ref197 doi: 10.1109/TMI.2019.2943841 – start-page: 512 year: 2020 ident: ref190 article-title: E2Net: An edge enhanced network for accurate liver and tumor segmentation on CT scans publication-title: Proc Int Conf Med Image Comput Comput Assist Intervent (MICCAI) – year: 2016 ident: ref81 article-title: Federated learning: Strategies for improving communication efficiency publication-title: arXiv 1610 05492 – start-page: 92 year: 2018 ident: ref84 article-title: Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation publication-title: Proc Int MICCAI Brainlesion Workshop – ident: ref246 doi: 10.1016/j.bpsc.2019.11.007 – start-page: 2672 year: 2014 ident: ref44 article-title: Generative adversarial nets publication-title: Proc Adv Neural Inf Process Syst – ident: ref19 doi: 10.1007/978-3-030-00934-2_79 – ident: ref95 doi: 10.1007/978-3-030-32245-8_14 – ident: ref26 doi: 10.1109/ACCESS.2017.2788044 – ident: ref136 doi: 10.1093/brain/awaa160 – start-page: 3347 year: 2019 ident: ref54 article-title: Transfusion: Understanding transfer learning for medical imaging publication-title: Proc Adv Neural Inf Process Syst – ident: ref67 doi: 10.1016/j.media.2019.02.009 – ident: ref143 doi: 10.1016/j.ebiom.2019.08.023 – ident: ref146 doi: 10.1007/978-3-319-68127-6_2 – ident: ref36 doi: 10.1001/jama.2016.17216 – ident: ref78 doi: 10.1007/978-3-030-00946-5_16 – year: 2020 ident: ref241 article-title: Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis publication-title: arXiv 2003 05037 – ident: ref171 doi: 10.1109/ACCESS.2019.2899635 – ident: ref51 doi: 10.1109/3DV.2019.00035 – ident: ref28 doi: 10.1016/j.media.2019.03.009 – ident: ref187 doi: 10.1016/j.media.2019.04.002 – ident: ref209 doi: 10.1007/s10278-019-00207-1 – start-page: 1050 year: 2016 ident: ref94 article-title: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning publication-title: Proc Int Conf Mach Learn – ident: ref45 doi: 10.1007/978-3-319-66179-7_58 – ident: ref216 doi: 10.1038/srep46450 – ident: ref107 doi: 10.1109/TMI.2018.2858202 – ident: ref116 doi: 10.1038/s41598-020-62148-y – ident: ref243 doi: 10.1016/j.media.2020.101800 – ident: ref220 doi: 10.1038/s41591-019-0508-1 – ident: ref151 doi: 10.1148/radiol.2018180940 – ident: ref242 doi: 10.1109/RBME.2020.2987975 – start-page: 2048 year: 2015 ident: ref46 article-title: Show, attend and tell: Neural image caption generation with visual attention publication-title: Proc Int Conf Mach Learn – ident: ref168 doi: 10.1016/j.media.2013.10.012 – volume: 1 year: 2000 ident: ref1 publication-title: Handbook of Medical Imaging – start-page: 1097 year: 2012 ident: ref33 article-title: ImageNet classification with deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref137 doi: 10.1016/j.neuroimage.2017.07.059 – ident: ref17 doi: 10.1002/mp.13764 – ident: ref177 doi: 10.1109/ICCV.2017.81 – ident: ref214 doi: 10.1109/TMI.2015.2458702 – start-page: 291 year: 2019 ident: ref60 article-title: 3D U2-net: A 3D universal U-net for multi-domain medical image segmentation publication-title: Proc Int Conf Med Image Comput Comput Assist Intervent (MICCIAI) – ident: ref215 doi: 10.4103/2153-3539.186902 – start-page: 101 year: 2017 ident: ref167 article-title: Automatic segmentation and disease classification using cardiac cine MR images publication-title: Statistical Atlases and Computational Models of the Heart ACDC and MMWHS Challenges – volume: 8 start-page: 1 year: 2018 ident: ref140 article-title: Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer's disease using structural MR and FDG-PET images publication-title: Sci Rep – ident: ref229 doi: 10.1038/s41591-019-0583-3 – ident: ref20 doi: 10.18653/v1/P18-1240 – year: 2018 ident: ref147 article-title: Motion artifact reduction in abdominal MR imaging using the U-NET network publication-title: Proc ICMRM Sci Meeting KSMRM – ident: ref86 doi: 10.1016/j.media.2020.101765 – volume: 63 year: 2020 ident: ref101 article-title: DR\|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images publication-title: Med Image Anal doi: 10.1016/j.media.2020.101715 – ident: ref165 doi: 10.1186/s12968-018-0471-x – ident: ref191 doi: 10.1117/1.JMI.5.4.044501 – ident: ref100 doi: 10.1016/j.media.2020.101724 – ident: ref169 doi: 10.1109/TMI.2017.2743464 – ident: ref72 doi: 10.1109/TMI.2019.2933425 – year: 2015 ident: ref201 article-title: CT lymph nodes publication-title: The Cancer Imaging Archive – ident: ref66 doi: 10.1016/j.media.2014.01.010 – ident: ref13 doi: 10.1007/978-3-030-59713-9_5 – year: 2018 ident: ref50 article-title: Neural architecture search: A survey publication-title: arXiv 1808 05377 – ident: ref236 doi: 10.1109/JPROC.2019.2943836 – ident: ref59 doi: 10.24963/ijcai.2018/96 – year: 2016 ident: ref164 article-title: A fully convolutional neural network for cardiac segmentation in short-axis MRI publication-title: arXiv 1604 00494 [cs] – ident: ref222 doi: 10.1016/S1470-2045(19)30739-9 – start-page: 2938 year: 2020 ident: ref83 article-title: How to backdoor federated learning publication-title: Proc Int Conf Artif Intell Statist – ident: ref34 doi: 10.1109/ISBI.2015.7163871 – ident: ref217 doi: 10.1371/journal.pone.0196828 – ident: ref175 doi: 10.1007/978-3-030-59725-2_45 – ident: ref159 doi: 10.3389/fcvm.2020.00017 – ident: ref77 doi: 10.1109/TMI.2020.2986242 – ident: ref76 doi: 10.1007/978-3-030-32226-7_31 – ident: ref162 doi: 10.1016/j.media.2019.04.007 – ident: ref115 doi: 10.1109/CVPR.2017.502 – year: 2019 ident: ref56 article-title: Med3D: Transfer learning for 3D medical image analysis publication-title: arXiv 1904 00625 – ident: ref237 doi: 10.1016/j.acra.2018.07.006 – year: 2014 ident: ref39 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv 1409 1556 – ident: ref75 doi: 10.1016/j.media.2019.101535 – ident: ref150 doi: 10.1109/TCI.2020.2964229 – ident: ref203 doi: 10.1007/978-3-030-59719-1_21 – year: 2015 ident: ref194 article-title: Data from pancreas-CT publication-title: The Cancer Imaging Archive – ident: ref124 doi: 10.1016/j.neuroimage.2017.04.039 – ident: ref131 doi: 10.1016/j.neuroimage.2018.08.003 – ident: ref212 doi: 10.1016/S2589-7500(20)30025-X – start-page: 562 year: 2015 ident: ref43 article-title: Deeply-supervised nets publication-title: Proc Artif Intell Statist – ident: ref200 doi: 10.1007/978-3-030-59719-1_56 – ident: ref221 doi: 10.1016/0090-4295(85)90280-8 – year: 2019 ident: ref11 publication-title: Med Image Comput Comput Assist Interv – year: 2020 ident: ref68 article-title: Marginal loss and exclusion loss for partially supervised multi-organ segmentation publication-title: arXiv 2007 03868 – ident: ref132 doi: 10.1109/TMI.2019.2897538 – ident: ref97 doi: 10.1007/978-3-030-32245-8_6 – ident: ref157 doi: 10.1007/978-3-319-24553-9_83 – ident: ref183 doi: 10.1109/TMI.2019.2905990 – ident: ref218 doi: 10.1001/jama.2017.14585 – ident: ref40 doi: 10.1109/CVPR.2015.7298594 – ident: ref123 doi: 10.1016/j.neuroimage.2016.01.024 – ident: ref186 doi: 10.1117/1.JMI.5.3.036501 – ident: ref25 doi: 10.1146/annurev-bioeng-071516-044442 – ident: ref199 doi: 10.1109/TMI.2015.2482920 – ident: ref210 doi: 10.1088/2057-1976/aad100 – ident: ref230 doi: 10.1016/S0168-8278(20)31254-X – ident: ref70 doi: 10.1007/978-3-030-20351-1_19 – ident: ref204 doi: 10.1002/mp.12399 – ident: ref138 doi: 10.1038/s41467-019-13163-9 – volume: 2 year: 2000 ident: ref8 publication-title: Handbook of Medical Imaging Medical Image Processing and Analysis – ident: ref79 doi: 10.1109/CVPR.2019.01076 – ident: ref98 doi: 10.1016/j.media.2019.101557 – ident: ref158 doi: 10.1007/978-3-030-00928-1_12 – ident: ref63 doi: 10.1007/978-3-319-66185-8_29 – ident: ref118 doi: 10.1148/radiol.2020201473 – ident: ref196 doi: 10.1016/j.jacr.2019.05.034 – ident: ref113 doi: 10.1038/srep46479 – ident: ref188 doi: 10.1109/TMI.2019.2948320 – ident: ref161 doi: 10.3389/fcvm.2019.00195 – ident: ref64 doi: 10.1007/978-3-030-00937-3_43 – ident: ref193 doi: 10.1016/j.media.2018.01.006 – ident: ref93 doi: 10.1016/j.media.2017.12.009 – year: 2008 ident: ref10 publication-title: Handbook of Medical Image Processing and Analysis – year: 2017 ident: ref152 article-title: 200x low-dose PET reconstruction using deep learning publication-title: arXiv 1712 04119 – ident: ref108 doi: 10.1609/aaai.v33i01.3301590 – ident: ref41 doi: 10.1109/CVPR.2016.90 – year: 2019 ident: ref173 article-title: Left ventricle full quantification challenge publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref5 doi: 10.1007/s00330-020-06672-5 – ident: ref47 doi: 10.1109/CVPR.2018.00745 – ident: ref96 doi: 10.1016/j.media.2019.04.014 – year: 2017 ident: ref24 publication-title: Deep learning for medical image analysis – ident: ref92 doi: 10.1016/j.media.2019.101619 – ident: ref31 doi: 10.1007/s00138-020-01060-x – ident: ref239 doi: 10.1016/j.crad.2014.09.017 – ident: ref37 doi: 10.1016/j.neucom.2018.09.013 – ident: ref207 doi: 10.1038/s41746-019-0104-2 – year: 2019 ident: ref109 article-title: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs publication-title: arXiv 1901 07042 – ident: ref73 doi: 10.1109/EMBC.2019.8857465 – ident: ref15 doi: 10.1016/j.media.2020.101693 – ident: ref247 doi: 10.1002/jmri.26907 – year: 2020 ident: ref174 article-title: Multi-centre, multi-vendor & multi-disease cardiac image segmentation challenge (M&MS) publication-title: Proc Med Image Comput Comput Assist Intervent (MICCAI) – ident: ref125 doi: 10.1016/j.neuroimage.2017.04.041 – ident: ref58 doi: 10.1109/CVPR.2018.00963 – ident: ref99 doi: 10.1007/978-3-030-32226-7_75 – ident: ref205 doi: 10.1038/s41598-020-61055-6 – ident: ref179 doi: 10.1007/978-3-030-00937-3_67 – year: 2017 ident: ref133 article-title: Non-rigid image registration using fully convolutional networks with deep self-supervision publication-title: arXiv 1709 00799 – ident: ref135 doi: 10.1016/j.neuroimage.2017.07.008 – ident: ref29 doi: 10.1007/s10278-019-00227-x – ident: ref22 doi: 10.1109/TMI.2016.2553401 – ident: ref128 doi: 10.1016/j.media.2016.05.004 – ident: ref198 doi: 10.1148/ryai.2020190102 – ident: ref62 doi: 10.1016/j.media.2020.101746 – start-page: 1015 year: 2014 ident: ref139 article-title: Early diagnosis of Alzheimer's disease with deep learning publication-title: Proc Int Symp Biomed Imag (ISBI) – ident: ref103 doi: 10.1109/TMI.2020.2994459 – ident: ref42 doi: 10.1109/CVPR.2017.243 – ident: ref18 doi: 10.1109/CVPR.2010.5540016 – ident: ref3 doi: 10.1016/S1076-6332(03)00671-8 – ident: ref232 doi: 10.1093/jnci/djx137 – ident: ref195 doi: 10.1016/j.compmedimag.2018.03.001 – ident: ref244 doi: 10.1016/j.neuroimage.2017.03.057 – ident: ref234 doi: 10.1158/1078-0432.CCR-18-2013 – year: 2020 ident: ref235 article-title: A hierarchical feature constraint to camouflage medical adversarial attacks publication-title: arXiv 2012 09501 – ident: ref231 doi: 10.1001/jamanetworkopen.2019.14645 – start-page: 120 year: 2017 ident: ref166 article-title: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features publication-title: Statistical Atlases and Computational Models of the Heart ACDC and MMWHS Challenges – volume: 296 year: 2020 ident: ref117 article-title: COVID-19 on the chest radiograph: A multi-reader evaluation of an AI system publication-title: Radiology doi: 10.1148/radiol.2020201874 – year: 2019 ident: ref111 publication-title: Lung CT Screening Reporting & Data System v1 1 – start-page: 7354 year: 2019 ident: ref48 article-title: Self-attention generative adversarial networks publication-title: Proc Int Conf Mach Learn – year: 2018 ident: ref49 article-title: Attention U-net: Learning where to look for the pancreas publication-title: arXiv 1804 03999 – ident: ref88 doi: 10.1007/978-3-030-32251-9_72 – year: 2013 ident: ref32 publication-title: MIT Tech Rev 2013 – ident: ref57 doi: 10.1007/978-3-319-59050-9_47 – ident: ref148 doi: 10.1109/ISBI.2018.8363679 – ident: ref160 doi: 10.1016/j.media.2019.06.001 – ident: ref202 doi: 10.1148/rg.2018170173 – year: 2018 ident: ref82 article-title: Federated learning with non-IID data publication-title: arXiv 1806 00582 – ident: ref225 doi: 10.1038/srep32706 – ident: ref112 doi: 10.1007/s00432-019-03098-5 – ident: ref114 doi: 10.1038/s41591-019-0447-x – ident: ref129 doi: 10.1016/j.media.2016.10.004 – ident: ref224 doi: 10.1016/S1470-2045(19)30793-4 – ident: ref178 doi: 10.1038/s42256-019-0019-2 – ident: ref55 doi: 10.1007/978-3-030-00934-2_94 – ident: ref192 doi: 10.1117/1.JMI.6.2.024007 – ident: ref228 doi: 10.1016/S0140-6736(19)32998-8 – ident: ref227 doi: 10.1038/s41591-019-0462-y – ident: ref163 doi: 10.1109/TPAMI.2016.2572683 – ident: ref89 doi: 10.1007/978-3-030-00934-2_52 – ident: ref104 doi: 10.1109/ISBI.2019.8759212 – year: 2019 ident: ref110 article-title: PadChest: A large chest X-ray image dataset with multi-label annotated reports publication-title: arXiv 1901 07441 – ident: ref61 doi: 10.1007/978-3-030-32251-9_42 – ident: ref240 doi: 10.1016/j.acra.2012.05.013 – ident: ref121 doi: 10.1016/j.neuroimage.2020.116819 – ident: ref141 doi: 10.1186/s40708-018-0080-3 – ident: ref80 doi: 10.1145/3298981 – ident: ref102 doi: 10.1109/ISBI.2018.8363518 – ident: ref85 doi: 10.1007/978-3-030-32692-0_16 – ident: ref226 doi: 10.1002/cyto.a.23065 – year: 2019 ident: ref119 article-title: Automated design of deep learning methods for biomedical image segmentation publication-title: arXiv 1904 08128 – ident: ref35 doi: 10.1109/TMI.2016.2528162 – ident: ref87 doi: 10.1073/pnas.1900654116 – ident: ref127 doi: 10.1016/j.neuroimage.2019.03.041 – year: 2017 ident: ref52 article-title: MobileNets: Efficient convolutional neural networks for mobile vision applications publication-title: arXiv 1704 04861 – ident: ref156 doi: 10.1016/j.compmedimag.2019.101684 – ident: ref14 doi: 10.1016/j.mri.2019.05.041 – year: 2018 ident: ref130 article-title: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge publication-title: arXiv 1811 02629 – ident: ref30 doi: 10.1109/JPROC.2019.2956422 – volume: 521 start-page: 436 year: 2015 ident: ref21 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: ref126 doi: 10.1016/j.neuroimage.2017.02.035 – ident: ref12 doi: 10.1088/978-0-7503-2216-4 – ident: ref153 doi: 10.1007/s10278-018-0150-3 – ident: ref144 doi: 10.1016/j.ebiom.2018.03.017 – ident: ref16 doi: 10.1088/1361-6560/ab843e – volume: 110 year: 2012 ident: ref6 publication-title: Computational Radiology and Imaging Therapy and Diagnostics – year: 2006 ident: ref9 publication-title: Medical Imaging Signals and Systems – ident: ref213 doi: 10.1038/s41571-019-0252-y – ident: ref223 doi: 10.1016/S1470-2045(19)30738-7 – ident: ref154 doi: 10.1007/978-3-030-05831-9_16 – ident: ref71 doi: 10.1007/978-3-030-00934-2_53 – ident: ref122 doi: 10.1016/j.neuroimage.2012.01.021 – ident: ref172 doi: 10.1007/978-3-030-00937-3_71 – ident: ref182 doi: 10.1007/978-3-319-46723-8_55 – start-page: 234 year: 2015 ident: ref38 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput Comput Assist Intervent (MICCAI) – ident: ref238 doi: 10.1148/radiol.10092437 – ident: ref106 doi: 10.3109/15412550903499522 – year: 2019 ident: ref185 article-title: A large annotated medical image dataset for the development and evaluation of segmentation algorithms publication-title: arXiv 1902 09063 – ident: ref53 doi: 10.1109/CVPR.2018.00716 – ident: ref189 doi: 10.1148/radiol.2019190512
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Snippet	Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging... Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging...
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SubjectTerms	Annotations Artificial intelligence Biomedical imaging Case studies Clinical diagnosis Computed tomography Computer architecture Deep learning Deep learning (DL) Digital imaging Diseases Image segmentation Literature reviews Medical diagnostic imaging Medical imaging Medical services Network architecture survey Trends
Title	A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
URI	https://ieeexplore.ieee.org/document/9363915 https://www.ncbi.nlm.nih.gov/pubmed/37786449 https://www.proquest.com/docview/2519972033 https://www.proquest.com/docview/2872181568 https://pubmed.ncbi.nlm.nih.gov/PMC10544772
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