Fetching the paper…
Reading the bibliography…
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the international conference on artificial intelligence and statistics
2010
Earlier work this paper cites.
B. Babenko, N. Verma, P. Dollár, and S. J. Belongie, “Multiple instance learning with manifold bags,” in ICML
2011
Earlier work this paper cites.
J. Amores, “Multiple instance classification: Review, taxonomy and comparative study,” Artificial intelligence
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
2014
Earlier work this paper cites.
P. O. Pinheiro and R. Collobert, “From image-level to pixel-level labeling with convolutional networks,” in CVPR
2015
Earlier work this paper cites.
J. Wu, Y. Yu, C. Huang, and K. Yu, “Deep multiple instance learning for image classification and auto-annotation,” in CVPR
2015
Earlier work this paper cites.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in ICCV
2015
Earlier work this paper cites.
M. Sun, T. X. Han, M.-C. Liu, and A. Khodayari-Rostamabad, “Multiple instance learning convolutional neural networks for object recognition,” in 2016 23rd International Conference on Pattern Recognition (ICPR)
2016
Earlier work this paper cites.
K. Sirinukunwattana, S. E. A. Raza, Y.-W. Tsang, D. R. Snead, I. A. Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images,” IEEE transactions on medical imaging
2016
Earlier work this paper cites.
M. Kandemir, M. Haussmann, F. Diego, K. T. Rajamani, J. Van Der Laak, and F. A. Hamprecht, “Variational weakly supervised gaussian processes.,” in BMVC
2016
Earlier work this paper cites.
L. Hou, D. Samaras, T. M. Kurc, Y. Gao, J. E. Davis, and J. H. Saltz, “Patch-based convolutional neural network for whole slide tissue image classification,” in CVPR
2016
Earlier work this paper cites.
G. Larsson, M. Maire, and G. Shakhnarovich, “Learning representations for automatic colorization,” in European conference on computer vision
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision
2016
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in CVPR
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR
2016
Cited alongside, same era.
G. Quellec, G. Cazuguel, B. Cochener, and M. Lamard, “Multiple-instance learning for medical image and video analysis,” IEEE reviews in biomedical engineering
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in CVPR
2019
Later among the works it cites.
S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord, “Boosting few-shot visual learning with self-supervision,” in ICCV
2019
Later among the works it cites.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data
2019
Later among the works it cites.
L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, “Self-supervised learning for medical image analysis using image context restoration,” Medical image analysis
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
J. Lemley, S. Bazrafkan, and P. Corcoran, “Smart augmentation learning an optimal data augmentation strategy,” Ieee Access
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in CVPR
2017
Cited alongside, same era.
V. Alex, K. Vaidhya, S. Thirunavukkarasu, C. Kesavadas, and G. Krishnamurthi, “Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation,” Journal of Medical Imaging
2017
Cited alongside, same era.
Z.-H. Zhou, “A brief introduction to weakly supervised learning,” National Science Review
2018
Cited alongside, same era.
M. Ilse, J. Tomczak, and M. Welling, “Attention-based deep multiple instance learning,” in International Conference on Machine Learning
2018
Cited alongside, same era.
L. Taylor and G. Nitschke, “Improving deep learning with generic data augmentation,” in 2018 IEEE Symposium Series on Computational Intelligence (SSCI)
2018
Cited alongside, same era.
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Synthetic data augmentation using gan for improved liver lesion classification,” in 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018)
2018
Cited alongside, same era.
2020
Later among the works it cites.
R. Yang, X. Li, H. Liu, Y. Zhen, X. Zhang, Q. Xiong, Y. Luo, C. Gao, and W. Zeng, “Chest ct severity score: an imaging tool for assessing severe covid-19,” Radiology: Cardiothoracic Imaging
2020
Later among the works it cites.
F. Shan, Y. Gao, J. Wang, W. Shi, N. Shi, M. Han, Z. Xue, D. Shen, and Y. Shi, “Abnormal lung quantification in chest ct images of covid-19 patients with deep learning and its application to severity prediction,” Med. Phys
2020
Later among the works it cites.
K. Li, Y. Fang, W. Li, C. Pan, P. Qin, Y. Zhong, X. Liu, M. Huang, Y. Liao, and S. Li, “Ct image visual quantitative evaluation and clinical classification of coronavirus disease (covid-19),” European radiology
2020
Later among the works it cites.
H. Chao, X. Fang, J. Zhang, F. Homayounieh, C. D. Arru, S. R. Digumarthy, R. Babaei, H. K. Mobin, I. Mohseni, L. Saba, et al
2020
Later among the works it cites.
G. Chassagnon, M. Vakalopoulou, E. Battistella, S. Christodoulidis, T.-N. Hoang-Thi, S. Dangeard, E. Deutsch, F. Andre, E. Guillo, N. Halm, et al
2020
Later among the works it cites.
Z. Han, B. Wei, Y. Hong, T. Li, J. Cong, X. Zhu, H. Wei, and W. Zhang, “Accurate screening of covid-19 using attention-based deep 3d multiple instance learning,” IEEE Transactions on Medical Imaging
2020
Later among the works it cites.
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, “Random erasing data augmentation.,” in AAAI
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Zhou, V. Sodha, J. Pang, M. B. Gotway, and J. Liang, “Models genesis,” Medical Image Analysis
2021
Closest in time.