Coronavirus Detection and Analysis on Chest CT with Deep Learning

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2014

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Ronneberger, O., Fischer, P. and Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. (2015)

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He, Kaiming, et al. ”Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016

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Cited alongside, same era.

Selvaraju, Ramprasaath R., et al. ”Grad-cam: Visual explanations from deep networks via gradient-based localization.” Proceedings of the IEEE international conference on computer vision. 2017

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Frid-Adar, M., Ben-Cohen, A., Amer, R., and Greenspan, H.: Improving the segmentation of anatomical structures in chest radiographs using u-net with an imagenet pre-trained encoder. In Image Analysis for Moving Organ, Breast, and Thoracic Images (pp. 159-168). Springer, Cham. (2018)

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ChainZ (www.ChainZ.cn)

Cited in the paper.

Gozes, Ophir, and Hayit Greenspan. ”Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset.” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019

2019

Later among the works it cites.

Bernheim, Adam, et al. ”Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection.” Radiology (2020): 200463

2020

Closest in time.