Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

W. Wu, X. Li, P. Du, G. Lang, M. Xu, K. Xu, and L. Li, “A deep learning system that generates quantitative ct reports for diagnosing pulmonary tuberculosis,” 2019. https://arxiv.org/abs/1910.02285

Original

1910

Earlier work this paper cites.

F. Shan, Y. Gao, J. Wang, W. Shi, N. Shi, M. Han, Z. Xue, and Y. Shi, “Lung infection quantification of covid-19 in ct images with deep learning,” 2020. https://arxiv.org/abs/2003.04655

Original

2003

Earlier work this paper cites.

L. Wang and A. Wong, “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images,” 2020. https://arxiv.org/abs/2003.09871v1

Original

2003

Earlier work this paper cites.

J. P. Cohen, P. Morrison, and L. Dao, “Covid-19 image data collection,” 2020. https://arxiv.org/abs/2003.11597

Original

2003

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR , vol. abs/1512.03385, 2015. http://arxiv.org/abs/1512.03385

Original

2015

Earlier work this paper cites.

F. Milletari, N. Navab, and S. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” CoRR , vol. abs/1606.04797, 2016. http://arxiv.org/abs/1606.04797

Original

2016

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. https://doi.org/10.1109/CVPR.2016.90

2016

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,” CoRR , vol. abs/1809.05989, 2018. http://arxiv.org/abs/1809.05989

Original

2018

Cited alongside, same era.

D. Kermany, M. Goldbaum, W. Cai, C. Valentim, H.-Y. Liang, S. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. Prasadha, J. Pei, M. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell , vol. 172, pp. 1122–1131.e9, 02 2018. https://doi.org/10.1016/j.cell.2018.02.010

2018

Cited alongside, same era.

X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, and J. Liu, “Chest ct for typical 2019-ncov pneumonia: Relationship to negative rt-pcr testing,” Radiology , vol. 0, no. 0, p. 200343, 0, pMID: 32049601. https://doi.org/10.1148/radiol.2020200343

2019

Cited alongside, same era.

X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Y. Chen, J. Su, G. Lang, Y. Li, H. Zhao, K. Xu, L. Ruan, and W. Wu, “Deep learning system to screen coronavirus disease 2019 pneumonia,” https://arxiv.org/abs/2002.09334 , 2020

M.-Y. Ng, E. Y. Lee, J. Yang, F. Yang, X. Li, H. Wang, M. M.-s. Lui, C. S.-Y. Lo, B. Leung, P.-L. Khong, C. K.-M. Hui, K.-y. Yuen, and M. D. Kuo, “Imaging profile of the covid-19 infection: Radiologic findings and literature review,” Radiology: Cardiothoracic Imaging , vol. 2, no. 1, p. e200034, 2020. https://doi.org/10.1148/ryct.2020200034

2020

Closest in time.

“Treatment for coronavirus disease (covid-19),” March 2020. https://www.healthline.com/health/coronavirus-treatment#available-treatment

2020

Closest in time.

“Covid-19: What we know so far about the 2019 novel coronavirus,” March 2020. https://www.uchicagomedicine.org/forefront/prevention-and-screening-articles/wuhan-coronavirus

2020

Closest in time.

F. Zhou, T. Yu, R. Du, G. Fan, Y. Liu, Z. Liu, J. Xiang, Y. Wang, B. Song, X. Gu, L. Guan, Y. Wei, H. Li, X. Wu, J. Xu, S. Tu, Y. Zhang, H. Chen, and B. Cao, “Clinical course and risk factors for mortality of adult inpatients with covid-19 in wuhan, china: a retrospective cohort study,” The Lancet , vol. 395, pp. 1054–1062, 3 2020. https://doi.org/10.1016/S0140-6736(20)30566-3