Fetching the paper…
Reading the bibliography…
The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world.
N. Qian, “On the momentum term in gradient descent learning algorithms,” Neural Networks , vol. 12, no. 1, pp. 145–151, 1999
1999
Earlier work this paper cites.
S. Armato III, G. McLennan, L. Bidaut, M. McNitt-Gray, C. Meyer, A. Reeves, B. Zhao, D. Aberle, C. Henschke, E. A. Hoffman, E. Kazerooni, H. MacMahon, E. van Beek, D. Yankelevitz, A. Biancardi, P. Bland, M. Brown, R. Engelmann, G. Laderach, D. Max, R. Pais, D. Qing, R. Roberts, A. Smith, A. Starkey, P. Batra, P. Caligiuri, A. Farooqi, G. Gladish, C. Jude, R. Munden, I. Petkovska, L. Quint, L. Schwartz, B. Sundaram, L. Dodd, C. Fenimore, D. Gur, N. Petrick, J. Freymann, J. Kirby, B. Hughes, A. Casteele, S. Gupte, M. Sallam, M. Heath, M. Kuhn, E. Dharaiya, R. Burns, D. Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, B. Croft, and L. Clarke, “Data from lidc-idri,” in The Cancer Imaging Archive , 2015. [Online]. Available: http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
2015
Earlier work this paper cites.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “"why should i trust you?": Explaining the predictions of any classifier,” 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision - ECCV 2016 , B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing, 2016, pp. 630–645
2016
Earlier work this paper cites.
D. Kumar, A. Wong, and G. W. Taylor, “Explaining the unexplained: A class-enhanced attentive response (clear) approach to understanding deep neural networks,” IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017
2017
Earlier work this paper cites.
S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” 2017
2017
Earlier work this paper cites.
D. Kumar, G. W. Taylor, and A. Wong, “Discovery radiomics with clear-dr: Interpretable computer aided diagnosis of diabetic retinopathy,” 2017
2017
Earlier work this paper cites.
A. Wong, M. J. Shafiee, B. Chwyl, and F. Li, “Ferminets: Learning generative machines to generate efficient neural networks via generative synthesis,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 8697–8710
2018
Earlier work this paper cites.
Z. Q. Lin, M. J. Shafiee, S. Bochkarev, M. S. Jules, X. Y. Wang, and A. Wong, “Do explanations reflect decisions? a machine-centric strategy to quantify the performance of explainability algorithms,” 2019
2019
Earlier work this paper cites.
M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in 2019 International Conference on Machine Learning (ICML) , 2019
2019
Earlier work this paper cites.
W. Wang, Y. Xu, R. Gao, R. Lu, K. Han, G. Wu, and W. Tan, “Detection of sars-cov-2 in different types of clinical specimens,” JAMA , vol. 323, no. 18, pp. 1843–1844, 05 2020
2020
Earlier work this paper cites.
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 , vol. 296, no. 2, pp. E115–E117, 2020, pMID: 32073353
2020
Earlier work this paper cites.
Y. Li, L. Yao, J. Li, L. Chen, Y. Song, Z. Cai, and C. Yang, “Stability issues of rt-pcr testing of sars-cov-2 for hospitalized patients clinically diagnosed with covid-19,” Journal of Medical Virology , vol. 92, no. 7, pp. 903–908, 2020
2020
Earlier work this paper cites.
Y. Yang, M. Yang, C. Shen, F. Wang, J. Yuan, J. Li, M. Zhang, Z. Wang, L. Xing, J. Wei, L. Peng, G. Wong, H. Zheng, M. Liao, K. Feng, J. Li, Q. Yang, J. Zhao, Z. Zhang, L. Liu, and Y. Liu, “Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-ncov infections,” medRxiv , 2020
2020
Cited alongside, same era.
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 for coronavirus disease 2019 (covid-19) in china: A report of 1014 cases,” Radiology , vol. 296, no. 2, pp. E32–E40, 2020, pMID: 32101510
2020
Cited alongside, same era.
X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, and J. Liu, “Chest ct for typical coronavirus disease 2019 (covid-19) pneumonia: Relationship to negative rt-pcr testing,” Radiology , vol. 296, no. 2, pp. E41–E45, 2020, pMID: 32049601
2020
Cited alongside, same era.
M. Rahimzadeh, A. Attar, and S. M. Sakhaei, “A fully automated deep learning-based network for detecting covid-19 from a new and large lung ct scan dataset,” medRxiv , 2020. [Online]. Available: https://www.medrxiv.org/content/early/2020/06/12/2020.06.08.20121541
2020
Later among the works it cites.
W. Ning, S. Lei, J. Yang et al. , “Open resource of clinical data from patients with pneumonia for the prediction of covid-19 outcomes via deep learning,” Nature Biomedical Engineering , vol. 4, pp. 1197–1207, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Morozov, A. Andreychenko, N. Pavlov, A. Vladzymyrskyy, N. Ledikhova, V. Gombolevskiy, I. Blokhin, P. Gelezhe, A. Gonchar, and V. Chernina, “Mosmeddata: Chest ct scans with covid-19 related findings dataset,” medRxiv , 2020. [Online]. Available: https://www.medrxiv.org/content/early/2020/05/22/2020.05.20.20100362
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
J. Shatri, L. Tafilaj, A. Turkaj, K. Dedushi1, M. Shatri, S. Bexheti, and S. K. Mucaj, “The role of chest computed tomography in asymptomatic patients of positive coronavirus disease 2019: A case and literature review,” Journal of Clinical Imaging Science , 2020
2020
Cited alongside, same era.
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, B. Du, L.-j. Li, G. Zeng, K.-Y. Yuen, R.-c. Chen, C.-l. Tang, T. Wang, P.-y. Chen, J. Xiang, S.-y. Li, J.-l. Wang, Z.-j. Liang, Y.-x. Peng, L. Wei, Y. Liu, Y.-h. Hu, P. Peng, J.-m. Wang, J.-y. Liu, Z. Chen, G. Li, Z.-j. Zheng, S.-q. Qiu, J. Luo, C.-j. Ye, S.-y. Zhu, and N.-s. Zhong, “Clinical characteristics of coronavirus disease 2019 in china,” New England Journal of Medicine , vol. 382, no. 18, pp. 1708–1720, 2020
2020
Cited alongside, same era.
D. Wang, B. Hu, C. Hu, F. Zhu, X. Liu, J. Zhang, B. Wang, H. Xiang, Z. Cheng, Y. Xiong, Y. Zhao, Y. Li, X. Wang, and Z. Peng, “Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in wuhan, china,” JAMA , vol. 323, no. 11, pp. 1061–1069, 03 2020
2020
Cited alongside, same era.
M. Chung, A. Bernheim, X. Mei, N. Zhang, M. Huang, X. Zeng, J. Cui, W. Xu, Y. Yang, Z. A. Fayad, A. Jacobi, K. Li, S. Li, and H. Shan, “Ct imaging features of 2019 novel coronavirus (2019-ncov),” Radiology , vol. 295, no. 1, pp. 202–207, 2020, pMID: 32017661
2020
Cited alongside, same era.
F. Pan, T. Ye, P. Sun, S. Gui, B. Liang, L. Li, D. Zheng, J. Wang, R. L. Hesketh, L. Yang, and C. Zheng, “Time course of lung changes at chest ct during recovery from coronavirus disease 2019 (covid-19),” Radiology , vol. 295, no. 3, pp. 715–721, 2020, pMID: 32053470
2020
Cited alongside, same era.
H. X. Bai, B. Hsieh, Z. Xiong, K. Halsey, J. W. Choi, T. M. L. Tran, I. Pan, L.-B. Shi, D.-C. Wang, J. Mei, X.-L. Jiang, Q.-H. Zeng, T. K. Egglin, P.-F. Hu, S. Agarwal, F.-F. Xie, S. Li, T. Healey, M. K. Atalay, and W.-H. Liao, “Performance of radiologists in differentiating covid-19 from non-covid-19 viral pneumonia at chest ct,” Radiology , vol. 296, no. 2, pp. E46–E54, 2020, pMID: 32155105
2020
Cited alongside, same era.
X. Mei, H.-C. Lee, K.-y. Diao, M. Huang, B. Lin, C. Liu, Z. Xie, Y. Ma, P. Robson, M. Chung, A. Bernheim, V. Mani, C. Calcagno, K. Li, S. Li, H. Shan, J. Lv, T. Zhao, J. Xia, and Y. Yang, “Artificial intelligence–enabled rapid diagnosis of patients with covid-19,” Nature Medicine , pp. 1–5, 05 2020
2020
Cited alongside, same era.
H. Gunraj, L. Wang, and A. Wong, “COVIDNet-CT: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images,” Frontiers in Medicine , 2020. [Online]. Available: https://doi.org/10.3389/fmed.2020.608525
2020
Cited alongside, same era.
2020
Later among the works it cites.
G. Erion, J. D. Janizek, P. Sturmfels, S. Lundberg, and S.-I. Lee, “Improving performance of deep learning models with axiomatic attribution priors and expected gradients,” 2020
2020
Later among the works it cites.
X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Q. Ni, Y. Chen, J. Su, G. Lang, Y. Li, H. Zhao, J. Liu, K. Xu, L. Ruan, J. Sheng, Y. Qiu, W. Wu, T. Liang, and L. Li, “A deep learning system to screen novel coronavirus disease 2019 pneumonia,” Engineering , 2020
2020
Later among the works it cites.
L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, K. Cao, D. Liu, G. Wang, Q. Xu, X. Fang, S. Zhang, J. Xia, and J. Xia, “Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary ct: Evaluation of the diagnostic accuracy,” Radiology , vol. 296, no. 2, pp. E65–E71, 2020, pMID: 32191588
2020
Later among the works it cites.
A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, “Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks,” Computers in Biology and Medicine , vol. 121, p. 103795, 2020
2020
Later among the works it cites.
V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, and N. Mehendale, “Diagnosis of covid-19 using ct scan images and deep learning techniques,” medRxiv , 2020
2020
Later among the works it cites.
J. Chen, L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, S. Hu, Y. Wang, X. Hu, B. Zheng, K. Zhang, H. Wu, Z. Dong, Y. Xu, Y. Zhu, X. Chen, L. Yu, and H. Yu, “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study,” medRxiv , 2020
2020
Later among the works it cites.
C. Zheng, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu, and X. Wang, “Deep learning-based detection for covid-19 from chest ct using weak label,” medRxiv , 2020
2020
Later among the works it cites.
S. Jin, B. Wang, H. Xu, C. Luo, L. Wei, W. Zhao, X. Hou, W. Ma, Z. Xu, Z. Zheng, W. Sun, L. Lan, W. Zhang, X. Mu, C. Shi, Z. Wang, J. Lee, Z. Jin, M. Lin, H. Jin, L. Zhang, J. Guo, B. Zhao, Z. Ren, S. Wang, Z. You, J. Dong, X. Wang, J. Wang, and W. Xu, “Ai-assisted ct imaging analysis for covid-19 screening: Building and deploying a medical ai system in four weeks,” medRxiv , 2020
2020
Later among the works it cites.
C. Jin, W. Chen, Y. Cao, Z. Xu, Z. Tan, X. Zhang, L. Deng, C. Zheng, J. Zhou, H. Shi, and J. Feng, “Development and evaluation of an ai system for covid-19 diagnosis,” medRxiv , 2020
2020
Later among the works it cites.
Y. Song, S. Zheng, L. Li, X. Zhang, X. Zhang, Z. Huang, J. Chen, H. Zhao, Y. Jie, R. Wang, Y. Chong, J. Shen, Y. Zha, and Y. Yang, “Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images,” medRxiv , 2020
2020
Later among the works it cites.
S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, and B. Xu, “A deep learning algorithm using ct images to screen for corona virus disease (covid-19),” medRxiv , 2020
2020
Later among the works it cites.
S. A. Harmon, T. H. Sanford et al. , “Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets,” Nature Communications , vol. 11, no. 4080, 2020
2020
Later among the works it cites.