Bibliography

The following articles and software packages were used in the development of MetaGenePipe.

Bie20

Lukas Biewald. Experiment tracking with weights and biases. 2020. Software available from wandb.com. URL: https://www.wandb.com/.

GG16

Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: representing model uncertainty in deep learning. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, 1050–1059. New York, New York, USA, 20–22 Jun 2016. PMLR. URL: https://proceedings.mlr.press/v48/gal16.html.

HSX+20

Stephanie A. Harmon, Thomas H. Sanford, Sheng Xu, Evrim B. Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, Maxime Blain, Michael Kassin, Dilara Long, Nicole Varble, Stephanie M. Walker, Ulas Bagci, Anna Maria Ierardi, Elvira Stellato, Guido Giovanni Plensich, Giuseppe Franceschelli, Cristiano Girlando, Giovanni Irmici, Dominic Labella, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Ronald M. Summers, Peter L. Choyke, Daguang Xu, Mona Flores, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Francesca Patella, Maurizio Cariati, Gianpaolo Carrafiello, Peng An, Bradford J. Wood, and Baris Turkbey. Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets. Nature Communications, 11(1):4080, 2020. URL: https://doi.org/10.1038/s41467-020-17971-2, doi:10.1038/s41467-020-17971-2.

HZRS16

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume, 770–778. 2016. doi:10.1109/CVPR.2016.90.

HSK+12

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. 2012. URL: https://arxiv.org/abs/1207.0580, doi:10.48550/ARXIV.1207.0580.

HXF+21

Junlin Hou, Jilan Xu, Rui Feng, Yuejie Zhang, Fei Shan, and Weiya Shi. Cmc-cov19d: contrastive mixup classification for covid-19 diagnosis. In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), volume, 454–461. 2021. doi:10.1109/ICCVW54120.2021.00055.

HG20

Jeremy Howard and Sylvain Gugger. Fastai: A Layered API for Deep Learning. Information, 2020. URL: https://www.mdpi.com/2078-2489/11/2/108, doi:10.3390/info11020108.

KCS+17a

Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman. The kinetics human action video dataset. 2017. URL: https://arxiv.org/abs/1705.06950, doi:10.48550/ARXIV.1705.06950.

KCS+17b

Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman. The kinetics human action video dataset. 2017. URL: https://arxiv.org/abs/1705.06950, doi:10.48550/ARXIV.1705.06950.

KB14

Diederik P. Kingma and Jimmy Ba. Adam: a method for stochastic optimization. 2014. URL: https://arxiv.org/abs/1412.6980, doi:10.48550/ARXIV.1412.6980.

KAK22

Dimitrios Kollias, Anastasios Arsenos, and Stefanos Kollias. Ai-mia: covid-19 detection & severity analysis through medical imaging. arXiv preprint arXiv:2206.04732, 2022.

KASK21

Dimitrios Kollias, Anastasios Arsenos, Levon Soukissian, and Stefanos Kollias. Mia-cov19d: covid-19 detection through 3-d chest ct image analysis. arXiv preprint arXiv:2106.07524, 2021.

KBV+20

Dimitrios Kollias, N Bouas, Y Vlaxos, V Brillakis, M Seferis, Ilianna Kollia, Levon Sukissian, James Wingate, and S Kollias. Deep transparent prediction through latent representation analysis. arXiv preprint arXiv:2009.07044, 2020.

KTS+18

Dimitrios Kollias, Athanasios Tagaris, Andreas Stafylopatis, Stefanos Kollias, and Georgios Tagaris. Deep neural architectures for prediction in healthcare. Complex & Intelligent Systems, 4(2):119–131, 2018.

KVS+20

Dimitris Kollias, Y Vlaxos, M Seferis, Ilianna Kollia, Levon Sukissian, James Wingate, and Stefanos D Kollias. Transparent adaptation in deep medical image diagnosis. In TAILOR, 251–267. 2020.

MMDB21

Radu Miron, Cosmin Moisii, Sergiu-Andrei Dinu, and Mihaela Breaban. COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB. ArXiv, 2021.

PGM+19

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: an imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\textquotesingle Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019. URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.

PVG+11

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.

PHTG22

Rosanna W Peeling, David L Heymann, Yik-Ying Teo, and Patricia J Garcia. Diagnostics for covid-19: moving from pandemic response to control. Lancet (London, England), 399:757–768, 2022. doi:10.1016/S0140-6736(21)02346-1.

See18

Euclid Seeram. Computed tomography: a technical review. Radiologic Technology, 89:279CT–302CT, 2018.

Smi18

Leslie N. Smith. A disciplined approach to neural network hyper-parameters: part 1 – learning rate, batch size, momentum, and weight decay. 2018. URL: https://arxiv.org/abs/1803.09820, doi:10.48550/ARXIV.1803.09820.

SVI+16

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume, 2818–2826. Los Alamitos, CA, USA, jun 2016. IEEE Computer Society. URL: https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.308, doi:10.1109/CVPR.2016.308.

TWT+18

Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. A closer look at spatiotemporal convolutions for action recognition. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, volume, 6450–6459. 2018. doi:10.1109/CVPR.2018.00675.

XZZ+20

Xingzhi Xie, Zheng Zhong, Wei Zhao, Chao Zheng, Fei Wang, and Jun Liu. Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology, 296(2):E41–E45, 2020. PMID: 32049601. URL: https://doi.org/10.1148/radiol.2020200343, arXiv:https://doi.org/10.1148/radiol.2020200343, doi:10.1148/radiol.2020200343.

WorldHOrganization

World Health Organization. Recommendations for national SARS-CoV-2 testing strategies and diagnostic capacities. https://www.who.int/publications/i/item/WHO-2019-nCoV-lab-testing-2021.1-eng. Accessed: 2022-06-27.