Fetching the paper…
Reading the bibliography…
This study proposed a novel framework for COVID-19 severity prediction, which is a combination of data-centric and model-centric approaches.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
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.
D. Krotov and J. J. Hopfield, “Dense associative memory for pattern recognition,” Advances in neural information processing systems , vol. 29, pp. 1172–1180, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
2017
Earlier work this paper cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8697–8710
2018
Earlier work this paper cites.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in Proceedings of the aaai conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 4780–4789
2019
Earlier work this paper cites.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 6105–6114
2019
Earlier work this paper cites.
Z. Feng, Q. Yu, S. Yao, L. Luo, W. Zhou, X. Mao, J. Li, J. Duan, Z. Yan, M. Yang et al. , “Early prediction of disease progression in covid-19 pneumonia patients with chest ct and clinical characteristics,” Nature communications , vol. 11, no. 1, pp. 1–9, 2020
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
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
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 et al. , “Time course of lung changes on chest ct during recovery from 2019 novel coronavirus (covid-19) pneumonia,” Radiology , 2020
2020
Cited alongside, same era.
A. Bernheim, X. Mei, M. Huang, Y. Yang, Z. A. Fayad, N. Zhang, K. Diao, B. Lin, X. Zhu, K. Li et al. , “Chest ct findings in coronavirus disease-19 (covid-19): relationship to duration of infection,” Radiology , p. 200463, 2020
2020
Cited alongside, same era.
M. E. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. Al Emadi et al. , “Can ai help in screening viral and covid-19 pneumonia?” IEEE Access , vol. 8, pp. 132 665–132 676, 2020
2020
Later among the works it cites.
H. Y. F. Wong, H. Y. S. Lam, A. H.-T. Fong, S. T. Leung, T. W.-Y. Chin, C. S. Y. Lo, M. M.-S. Lui, J. C. Y. Lee, K. W.-H. Chiu, T. W.-H. Chung et al. , “Frequency and distribution of chest radiographic findings in patients positive for covid-19,” Radiology , vol. 296, no. 2, pp. E72–E78, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
“Wikipedia,” 2021
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Liu, Q. Zhang, C. Huang, C. Shi, L. Wang, N. Shi, C. Fang, F. Shan, X. Mei, J. Shi et al. , “Ct quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of covid-19 patients,” Theranostics , vol. 10, no. 12, p. 5613, 2020
2020
Cited alongside, same era.
H. Panwar, P. Gupta, M. K. Siddiqui, R. Morales-Menendez, and V. Singh, “Application of deep learning for fast detection of covid-19 in x-rays using ncovnet,” Chaos, Solitons & Fractals , vol. 138, p. 109944, 2020
2020
Cited alongside, same era.
J. P. Cohen, L. Dao, K. Roth, P. Morrison, Y. Bengio, A. F. Abbasi, B. Shen, H. K. Mahsa, M. Ghassemi, H. Li et al. , “Predicting covid-19 pneumonia severity on chest x-ray with deep learning,” Cureus , vol. 12, no. 7, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213–229
2020
Cited alongside, same era.
I. Misra and L. v. d. Maaten, “Self-supervised learning of pretext-invariant representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6707–6717
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. L. Booth, E. Abels, and P. McCaffrey, “Development of a prognostic model for mortality in covid-19 infection using machine learning,” Modern Pathology , vol. 34, no. 3, pp. 522–531, 2021
2021
Closest in time.
N. Lassau, S. Ammari, E. Chouzenoux, H. Gortais, P. Herent, M. Devilder, S. Soliman, O. Meyrignac, M.-P. Talabard, J.-P. Lamarque et al. , “Integrating deep learning ct-scan model, biological and clinical variables to predict severity of covid-19 patients,” Nature communications , vol. 12, no. 1, pp. 1–11, 2021
2021
Closest in time.
M. Fridadar, R. Amer, O. Gozes, J. Nassar, and H. Greenspan, “Covid-19 in cxr: From detection and severity scoring to patient disease monitoring,” IEEE journal of biomedical and health informatics , 2021
2021
Closest in time.
2021
Closest in time.
J. M. C. Nam Nguyen, “Attention learning for classification of dermoscopy image,” arXiv preprint , 2021
2021
Closest in time.
2021
Closest in time.
T. Rahman, A. Khandakar, Y. Qiblawey, A. Tahir, S. Kiranyaz, S. B. A. Kashem, M. T. Islam, S. Al Maadeed, S. M. Zughaier, M. S. Khan et al. , “Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images,” Computers in biology and medicine , vol. 132, p. 104319, 2021
2021
Closest in time.
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2097–2106
2097
Closest in time.