Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020
Tài liệu tham khảo
Margulis, 1981, Whitehouse lecture. Radiologic imaging: changing costs, greater benefits, AJR Am. J. Roentgenol., 136, 657, 10.2214/ajr.136.4.657
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Bach, 2007, Computed tomography screening and lung cancer outcomes, JAMA, 297, 953, 10.1001/jama.297.9.953
Negendank, 1992, Studies of human tumors by MRS: a review, NMR Biomed, 5, 303, 10.1002/nbm.1940050518
Raichle, 1985, Positron emission tomography. Progress in brain imaging, Nature, 317, 574, 10.1038/317574a0
Havaei, 2017, Brain tumor segmentation with deep neural networks, Med. Image Anal., 35, 18, 10.1016/j.media.2016.05.004
Zhang, 2019, Medical image classification using synergic deep learning, Med. Image Anal., 54, 10, 10.1016/j.media.2019.02.010
Schmuelling, 2021, Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: no significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation, Eur. J. Radiol., 141, 109816, 10.1016/j.ejrad.2021.109816
Zhao, 2018, A deep learning model integrating FCNNs and CRFs for brain tumor segmentation, Med. Image Anal., 43, 98, 10.1016/j.media.2017.10.002
Bao, 2018, 3D randomized connection network with graph-based label inference, IEEE Trans. Image Process.: Publ. IEEE Signal Process. Soc., 27, 3883, 10.1109/TIP.2018.2829263
Chang, 2018, Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging, Clin. Cancer Res.: Offic. J. Am. Assoc. Cancer Res., 24, 1073, 10.1158/1078-0432.CCR-17-2236
Biswas, 2019, State-of-the-art review on deep learning in medical imaging, Front. Biosci. (Landmark Ed.), 24, 392, 10.2741/4725
Zhang, 2021, High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules, Eur. J. Radiol., 141, 109810, 10.1016/j.ejrad.2021.109810
Litjens, 2017, A survey on deep learning in medical image analysis, Med. Image Anal., 42, 60, 10.1016/j.media.2017.07.005
Krizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun. Acm., 60, 84, 10.1145/3065386
Yamanakkanavar, 2020, MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer's disease: a survey, Sensors (Basel), 20, 3243, 10.3390/s20113243
Ding, 2019, A deep learning model to predict a diagnosis of alzheimer disease by using F-18-FDG PET of the Brain, Radiology, 290, 456, 10.1148/radiol.2018180958
Ortiz, 2016, Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease, Int. J. Neural Syst., 26, 1650025, 10.1142/S0129065716500258
Kim, 2018, Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine, Human Brain Map., 39, 3728, 10.1002/hbm.24207
Yi, 2019, Generative adversarial network in medical imaging: a review, Med. Image Anal., 58, 101552, 10.1016/j.media.2019.101552
Becker, 2019, Injecting and removing suspicious features in breast imaging with CycleGAN: a pilot study of automated adversarial attacks using neural networks on small images, Eur. J. Radiol., 120, 108649, 10.1016/j.ejrad.2019.108649
Wang, 2021, Deep learning in medical ultrasound image analysis: a review, IEEE Access, 9, 54310, 10.1109/ACCESS.2021.3071301
Karimi, 2020, Deep learning with noisy labels: exploring techniques and remedies in medical image analysis, Med Image Anal, 65, 101759, 10.1016/j.media.2020.101759
William, 2001, The literature of bibliometrics, scientometrics, and informetrics, Scientometrics, 52, 291, 10.1023/A:1017919924342
Guler, 2016, Scientific workflows for bibliometrics, Scientometrics, 107, 385, 10.1007/s11192-016-1885-6
Cheng, 2000, Biclustering of expression data, Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 93
Bhattacharya, 1998, PK, Mapping a research area at the micro level using co-word analysis, Scientometrics, 43, 359, 10.1007/BF02457404
Coletti, 2001, Medical subject headings used to search the biomedical literature, J. Am. Med. Inform. Assoc.: JAMIA, 8, 317, 10.1136/jamia.2001.0080317
Zhang, 2017, Radiology research in mainland China in the past 10 years: a survey of original articles published in Radiology and European Radiology, Eur. Radiol., 27, 4379, 10.1007/s00330-016-4689-4
Schiaffino, 2020, Upgrade rate of percutaneously diagnosed pure atypical ductal hyperplasia: systematic review and meta-analysis of 6458 lesions, Radiology, 294, 76, 10.1148/radiol.2019190748
L.W. Cui, L. Yan, H. Zhang, Y.F. Hou, Y.N. Huang, et al., Development of a Text Mining System based on the Co-occurrence of Bibliographic Items in Literature, New Technology of Library and Information Service, 2008, pp. 70–75.
K. Lab, Webcite gCLUTO-Graphical Clustering Toolkit, WEBC GCLUTO GRAPH CL.
Law, 1988, Policy and the mapping of scientific change: a co-word analysis of research into environmental acidification, Scientometrics, 14, 251, 10.1007/BF02020078
van Eck, 2009, Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics, 84, 523, 10.1007/s11192-009-0146-3
Bornmann, 2018, Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis, Scientometrics, 114, 427, 10.1007/s11192-017-2591-8
Waltman, 2010, A unified approach to mapping and clustering of bibliometric networks, J. Inform., 4, 629, 10.1016/j.joi.2010.07.002
Shen, 2017, Deep learning in medical image analysis, Ann. Rev. Biomed. Eng., 19, 221, 10.1146/annurev-bioeng-071516-044442
Cai, 2020, A review of the application of deep learning in medical image classification and segmentation, Ann. Transl. Med., 8, 713, 10.21037/atm.2020.02.44
Choi, 2020, IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation, Eu. J. Radiol., 128, 109031, 10.1016/j.ejrad.2020.109031
Javor, 2020, Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography, Eur. J. Radiol., 133, 109402, 10.1016/j.ejrad.2020.109402
Jin, 2020, Development and evaluation of an artificial intelligence system for COVID-19 diagnosis, Nature Commun., 11, 5088, 10.1038/s41467-020-18685-1
Zhang, 2019, Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation, Comput. Biol. Med., 108, 223, 10.1016/j.compbiomed.2019.03.011
Gong, 2021, A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records, Eur. J. Radiol., 139, 109583, 10.1016/j.ejrad.2021.109583
A. Radford, L. Metz, S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015, p. arXiv:1511.06434.
S.C. Martin Arjovsky, Léon Bottou, Wasserstein generative adversarial networks, in: International Conference on Machine Learning vol. 70, 2017, pp. 214–223.
D. Berthelot, T. Schumm, L. Metz, BEGAN: Boundary Equilibrium Generative Adversarial Networks, 2017, pp. arXiv:1703.10717.
Wang, 2021, Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures, Eur. J. Nucl. Med. Mole. Imaging, 48, 1478, 10.1007/s00259-020-05075-4
Loey, 2020, Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning, Symmetry-Basel, 12, 651, 10.3390/sym12040651
Song, 2020, Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage IV EGFR variant–positive non–small cell lung cancer, JAMA Netw. Open, 3, 10.1001/jamanetworkopen.2020.30442
Schwyzer, 2018, Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – initial results, Lung Cancer, 126, 170, 10.1016/j.lungcan.2018.11.001
Tajbakhsh, 2016, Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 1299, 10.1109/TMI.2016.2535302