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The Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020.
J. Vollmer, R. Mencl, and H. Muller, “Improved Laplacian Smoothing of Noisy Surface Meshes,” Computer Graphics Forum , 1999
1999
Earlier work this paper cites.
N. Stolte, “Graphics using implicit surfaces with interval arithmetic based recursive voxelization,” in Proceedings of the Sixth IASTED International Conference on Computer Graphics and Imaging, Honolulu, Hawaii, USA, August 13-15, 2003 . IASTED/ACTA Press, 2003, pp. 200–205
2003
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
National Lung Screening Trial Research Team, “The national lung screening trial: overview and study design,” Radiology , vol. 258, no. 1, pp. 243–253, 2011
2011
Earlier work this paper cites.
E. A. Regan, J. E. Hokanson, J. R. Murphy, B. Make, D. A. Lynch, T. H. Beaty, D. Curran-Everett, E. K. Silverman, and J. D. Crapo, “Genetic epidemiology of COPD (COPDGene) study design,” COPD: Journal of Chronic Obstructive Pulmonary Disease , vol. 7, no. 1, pp. 32–43, 2011
2011
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) . IEEE, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Nie, R. Trullo, J. Lian, C. Petitjean, S. Ruan, Q. Wang, and D. Shen, “Medical image synthesis with context-aware generative adversarial networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017
2017
Earlier work this paper cites.
A. Chartsias, T. Joyce, R. Dharmakumar, and S. A. Tsaftaris, “Adversarial image synthesis for unpaired multi-modal cardiac data,” in International workshop on simulation and synthesis in medical imaging . Springer, 2017
2017
Earlier work this paper cites.
D. Yang, D. Xu, S. K. Zhou, B. Georgescu, M. Chen, S. Grbic, D. Metaxas, and D. Comaniciu, “Automatic liver segmentation using an adversarial image-to-image network,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 507–515
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Wang, G. Macnaught, G. Papanastasiou, T. MacGillivray, and D. Newby, “Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks,” in International Workshop on Simulation and Synthesis in Medical Imaging . Springer, 2018
2018
Earlier work this paper cites.
H. Yang, J. Sun, A. Carass, C. Zhao, J. Lee, Z. Xu, and J. Prince, “Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Springer, 2018
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
D. Jin, Z. Xu, Y. Tang, A. P. Harrison, and D. J. Mollura, “CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 732–740
2018
Cited alongside, same era.
H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole, and M. Michalski, “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” in International workshop on simulation and synthesis in medical imaging . Springer, 2018
2018
Cited alongside, same era.
J. P. Kanne, B. P. Little, J. H. Chung, B. M. Elicker, and L. H. Ketai, “Essentials for radiologists on COVID-19: an update—radiology scientific expert panel,” Radiology , p. 200527, 2020
2020
Closest in time.
W.-j. Guan, Z.-y. Ni, Y. Hu, W.-h. Liang, C.-q. Ou, J.-x. He, L. Liu, H. Shan, C.-l. Lei, D. S. Hui et al. , “Clinical characteristics of coronavirus disease 2019 in china,” New England Journal of Medicine , 2020
2020
Closest in time.
K. Mizumoto, K. Kagaya, A. Zarebski, and G. Chowell, “Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020,” Eurosurveillance , vol. 25, no. 10, 2020
2020
Closest in time.
Y. Ji, Z. Ma, M. P. Peppelenbosch, and Q. Pan, “Potential association between COVID-19 mortality and health-care resource availability,” The Lancet Global Health , vol. 8, no. 4, p. e480, 2020
2020
Closest in time.
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M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neurocomputing , vol. 321, pp. 321–331, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. P. Cohen, M. Luck, and S. Honari, “Distribution matching losses can hallucinate features in medical image translation,” in International conference on medical image computing and computer-assisted intervention . Springer, 2018
2018
Cited alongside, same era.
J. Yang, S. Liu, S. Grbic, A. A. A. Setio, Z. Xu, E. Gibson, G. Chabin, B. Georgescu, A. F. Laine, and D. Comaniciu, “Class-aware adversarial lung nodule synthesis in CT images,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) . IEEE, 2019, pp. 1348–1352
2019
Cited alongside, same era.
Z. Xu, X. Wang, H.-C. Shin, H. Roth, D. Yang, F. Milletari, L. Zhang, and D. Xu, “Tunable CT lung nodule synthesis conditioned on background image and semantic features,” in International Workshop on Simulation and Synthesis in Medical Imaging . Springer, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
C. Gao, S. Clark, J. Furst, and D. Raicu, “Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection,” in Medical Imaging 2019: Computer-Aided Diagnosis , vol. 10950. International Society for Optics and Photonics, 2019
2019
Cited alongside, same era.
C. Han, Y. Kitamura, A. Kudo, A. Ichinose, L. Rundo, Y. Furukawa, K. Umemoto, Y. Li, and H. Nakayama, “Synthesizing diverse lung nodules wherever massively: 3D multi-conditional GAN-based CT image augmentation for object detection,” in 2019 International Conference on 3D Vision (3DV) . IEEE, 2019
2019
Cited alongside, same era.
Q. Wang, X. Zhou, C. Wang, Z. Liu, J. Huang, Y. Zhou, C. Li, H. Zhuang, and J.-Z. Cheng, “WGAN-based synthetic minority over-sampling technique: improving semantic fine-grained classification for lung nodules in CT images,” IEEE Access , vol. 7, 2019
2019
Cited alongside, same era.
E. J. Emanuel, G. Persad, R. Upshur, B. Thome, M. Parker, A. Glickman, C. Zhang, C. Boyle, M. Smith, and J. P. Phillips, “Fair Allocation of Scarce Medical Resources in the Time of COVID-19,” New England Journal of Medicine , 2020
2020
Closest in time.
Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, and W. Ji, “Sensitivity of chest CT for COVID-19: comparison to RT-PCR,” Radiology , 2020
2020
Closest in time.
T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, and L. Xia, “Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiology , p. 200642, 2020
2020
Closest in time.
G. D. Rubin, C. J. Ryerson, L. B. Haramati, N. Sverzellati, J. P. Kanne, S. Raoof, N. W. Schluger, A. Volpi, J.-J. Yim, I. B. Martin et al. , “The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society,” Chest , 2020
2020
Closest in time.
2020
Closest in time.
L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song et al. , “Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT,” Radiology , p. 200905, 2020
2020
Closest in time.
S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng et al. , “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19),” medRxiv , 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
S. Inui, A. Fujikawa, M. Jitsu, N. Kunishima, S. Watanabe, Y. Suzuki, S. Umeda, and Y. Uwabe, “Chest CT findings in cases from the cruise ship “Diamond Princess” with coronavirus disease 2019 (COVID-19),” Radiology: Cardiothoracic Imaging , vol. 2, no. 2, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.