
@incollection{Bengio+chapter2007,
author = {Bengio, Yoshua and LeCun, Yann},
booktitle = {Large Scale Kernel Machines},
publisher = {MIT Press},
title = {Scaling Learning Algorithms Towards {AI}},
year = {2007}
}

@article{Hinton06,
author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
journal = {Neural Computation},
pages = {1527--1554},
title = {A Fast Learning Algorithm for Deep Belief Nets},
volume = {18},
year = {2006}
}

@book{goodfellow2016deep,
title={Deep learning},
author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
volume={1},
year={2016},
publisher={MIT Press}
}

% MoCo V1
@Article{he2019moco,
  author  = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick},
  title   = {Momentum Contrast for Unsupervised Visual Representation Learning},
  journal = {arXiv preprint arXiv:1911.05722},
  year    = {2019},
}

% MoCo V2
@Article{chen2020mocov2,
  author  = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He},
  title   = {Improved Baselines with Momentum Contrastive Learning},
  journal = {arXiv preprint arXiv:2003.04297},
  year    = {2020},
}

% CheXpert
@inproceedings{irvin2019chexpert,
  title={Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={590--597},
  year={2019}
}

% MIMIC-CXR-JPG
@article{mimic2019,
  author    = {Alistair E. W. Johnson and
               Tom J. Pollard and
               Seth J. Berkowitz and
               Nathaniel R. Greenbaum and
               Matthew P. Lungren and
               Chih{-}ying Deng and
               Roger G. Mark and
               Steven Horng},
  title     = {{MIMIC-CXR:} {A} large publicly available database of labeled chest
               radiographs},
  journal   = {CoRR},
  volume    = {abs/1901.07042},
  year      = {2019},
  url       = {http://arxiv.org/abs/1901.07042},
  archivePrefix = {arXiv},
  eprint    = {1901.07042},
  timestamp = {Fri, 01 Feb 2019 13:39:59 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1901-07042.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

% Shenzhen dataset
@article{jaeger2014two,
  title={Two public chest X-ray datasets for computer-aided screening of pulmonary diseases},
  author={Jaeger, Stefan and Candemir, Sema and Antani, Sameer and W{\'a}ng, Y{\`\i}-Xi{\'a}ng J and Lu, Pu-Xuan and Thoma, George},
  journal={Quantitative imaging in medicine and surgery},
  volume={4},
  number={6},
  pages={475},
  year={2014},
  publisher={AME Publications}
}


% ChestXray14
@InProceedings{chestxray14,
author    = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald},
title = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)},
pages     = {3462--3471},
year      = {2017}}
    
    
% ResNet
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

% DenseNet
@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

% ImageNet
@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}

% Siamese Network
@inproceedings{koch2015siamese,
  title={Siamese neural networks for one-shot image recognition},
  author={Koch, Gregory and Zemel, Richard and Salakhutdinov, Ruslan},
  booktitle={ICML deep learning workshop},
  volume={2},
  year={2015},
  organization={Lille}
}



@article{miyato2018virtual,
  title={Virtual adversarial training: a regularization method for supervised and semi-supervised learning},
  author={Miyato, Takeru and Maeda, Shin-ichi and Koyama, Masanori and Ishii, Shin},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={41},
  number={8},
  pages={1979--1993},
  year={2018},
  publisher={IEEE}
}

% Survey on chest-xray deep learning methods
@article{monshi2020deep,
  title={Deep learning in generating radiology reports: A survey},
  author={Monshi, Maram Mahmoud A and Poon, Josiah and Chung, Vera},
  journal={Artificial Intelligence in Medicine},
  pages={101878},
  year={2020},
  publisher={Elsevier}
}

% FixMatch
@article{sohn2020fixmatch,
  title={Fixmatch: Simplifying semi-supervised learning with consistency and confidence},
  author={Sohn, Kihyuk and Berthelot, David and Li, Chun-Liang and Zhang, Zizhao and Carlini, Nicholas and Cubuk, Ekin D and Kurakin, Alex and Zhang, Han and Raffel, Colin},
  journal={arXiv preprint arXiv:2001.07685},
  year={2020}
}

% PIRL
@inproceedings{misra2020self,
  title={Self-supervised learning of pretext-invariant representations},
  author={Misra, Ishan and Maaten, Laurens van der},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6707--6717},
  year={2020}
}

% InfoNCE
@article{oord2018representation,
  title={Representation learning with contrastive predictive coding},
  author={Oord, Aaron van den and Li, Yazhe and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1807.03748},
  year={2018}
}
% Mimic-cxr
@article{JohnsonMimic,
  author    = {Alistair E. W. Johnson and
               Tom J. Pollard and
               Seth J. Berkowitz and
               Nathaniel R. Greenbaum and
               Matthew P. Lungren and
               Chih{-}ying Deng and
               Roger G. Mark and
               Steven Horng},
  title     = {{MIMIC-CXR:} {A} large publicly available database of labeled chest
               radiographs},
  journal   = {CoRR},
  volume    = {abs/1901.07042},
  year      = {2019},
  url       = {http://arxiv.org/abs/1901.07042},
  archivePrefix = {arXiv},
  eprint    = {1901.07042},
  timestamp = {Fri, 01 Feb 2019 13:39:59 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1901-07042.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

% PadChest
@article{Bustos2020PadChestAL,
  title={PadChest: A large chest x-ray image dataset with multi-label annotated reports},
  author={Aurelia Bustos and A. Pertusa and J. M. Salinas and M. Iglesia-Vay{\'a}},
  journal={Medical image analysis},
  year={2020},
  volume={66},
  pages={
          101797
        }
}

% chexnet
@article{rajpurkar2017chexnet,
  title={Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning},
  author={Rajpurkar, Pranav and Irvin, Jeremy and Zhu, Kaylie and Yang, Brandon and Mehta, Hershel and Duan, Tony and Ding, Daisy and Bagul, Aarti and Langlotz, Curtis and Shpanskaya, Katie and others},
  journal={arXiv preprint arXiv:1711.05225},
  year={2017}
}


% difference between imagenet and medical images
% transfer learning, medical imaging
@article{raghu2019transfusion,
  title={Transfusion: Understanding Transfer Learning for Medical Imaging},
  author={Raghu, Maithra and Zhang, Chiyuan and Kleinberg, Jon and Bengio, Samy},
  journal={arXiv preprint arXiv:1902.07208},
  year={2019}
}

@article{livieris2019detecting,
  title={Detecting Lung Abnormalities From X-rays Using an Improved SSL Algorithm},
  author={Livieris, Ioannis and Kanavos, Andreas and Pintelas, Panagiotis},
  journal={Electronic Notes in Theoretical Computer Science},
  volume={343},
  pages={19--33},
  year={2019},
  publisher={Elsevier}
}


% OpenI dataset
% https://openi.nlm.nih.gov/gridquery?q=coronavirus&it=xg&m=1&n=100
@article{demner2016preparing,
  title={Preparing a collection of radiology examinations for distribution and retrieval},
  author={Demner-Fushman, Dina and Kohli, Marc D and Rosenman, Marc B and Shooshan, Sonya E and Rodriguez, Laritza and Antani, Sameer and Thoma, George R and McDonald, Clement J},
  journal={Journal of the American Medical Informatics Association},
  volume={23},
  number={2},
  pages={304--310},
  year={2016},
  publisher={Oxford University Press}
}

%Jigsaw
@inproceedings{noroozi2016unsupervised,
  title={Unsupervised learning of visual representations by solving jigsaw puzzles},
  author={Noroozi, Mehdi and Favaro, Paolo},
  booktitle={European Conference on Computer Vision},
  pages={69--84},
  year={2016},
  organization={Springer}
}

% Relative patch prediction
@inproceedings{doersch2015unsupervised,
  title={Unsupervised visual representation learning by context prediction},
  author={Doersch, Carl and Gupta, Abhinav and Efros, Alexei A},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1422--1430},
  year={2015}
}

% Contrastive learning with different colorization
@article{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  journal={arXiv preprint arXiv:2002.05709},
  year={2020}
}

% general imagenet challenge
@InProceedings{Kornblith_2019_CVPR,
author = {Kornblith, Simon and Shlens, Jonathon and Le, Quoc V.},
title = {Do Better ImageNet Models Transfer Better?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

% Number of chest x-ray images
@article{raoof2012interpretation,
  title={Interpretation of plain chest roentgenogram},
  author={Raoof, Suhail and Feigin, David and Sung, Arthur and Raoof, Sabiha and Irugulpati, Lavanya and Rosenow III, Edward C},
  journal={Chest},
  volume={141},
  number={2},
  pages={545--558},
  year={2012},
  publisher={Elsevier}
}



% random is just as good as ImageNet
@article{He2018ImageNet,
  title={Rethinking ImageNet Pre-training},
  author={He, Kaiming and Girshick, Ross and Dollar, Piotr},
  journal={arXiv preprint arXiv:1811.08883},
  year={2018}
}

% how many x-rays done per year
@online{ImvXrays,
  author = {Young, Lorna},
  title = {Highlights from IMV 2019 X-ray/DR/CR Outlook Report},
  year = 2019,
  url = {https://imvinfo.com/highlights-imv-2019-x-ray-dr-cr-outlook-report/},
  urldate = {2020-08-01}
}

% unlabeled x-ray images
@article{baltruschat2019comparison,
  title={Comparison of deep learning approaches for multi-label chest X-ray classification},
  author={Baltruschat, Ivo M and Nickisch, Hannes and Grass, Michael and Knopp, Tobias and Saalbach, Axel},
  journal={Scientific reports},
  volume={9},
  number={1},
  pages={1--10},
  year={2019},
  publisher={Nature Publishing Group}
}


% simclr v2
@article{Chen2020Simclrv2,
  title={Big Self-Supervised Models are Strong Semi-Supervised Learners},
  author={Chen, Ting and Kornblith, Simon and Swersky, Kevin and Norouzi, Mohammad and Hinton, Geoffrey},
  journal={arXiv preprint arXiv:2006.10029},
  year={2020}
}

% semi-supervised  medical
@article{cheplygina2019not,
  title={Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis},
  author={Cheplygina, Veronika and de Bruijne, Marleen and Pluim, Josien PW},
  journal={Medical image analysis},
  volume={54},
  pages={280--296},
  year={2019},
  publisher={Elsevier}
}


% linear eval
@inproceedings{bachman2019learning,
  title={Learning representations by maximizing mutual information across views},
  author={Bachman, Philip and Hjelm, R Devon and Buchwalter, William},
  booktitle={Advances in Neural Information Processing Systems},
  pages={15535--15545},
  year={2019}
}



%random is equal to imagenet
@InProceedings{He_2019_ICCV,
author = {He, Kaiming and Girshick, Ross and Dollar, Piotr},
title = {Rethinking ImageNet Pre-Training},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
%supervised contrastive
@Article{khosla2020supervised,
    title   = {Supervised Contrastive Learning},
    author  = {Prannay Khosla and Piotr Teterwak and Chen Wang and Aaron Sarna and Yonglong Tian and Phillip Isola and Aaron Maschinot and Ce Liu and Dilip Krishnan},
    journal = {arXiv preprint arXiv:2004.11362},
    year    = {2020},
}

% unsupervised generalization
@article{erhan2010unsupervised,
  title={Why Does Unsupervised Pre-training Help Deep Learning?},
  author={Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Manzagol, Pierre-Antoine and Vincent, Pascal and Bengio, Samy},
  journal = {Journal of Machine Learning Research},
  year    = 2010,
  number  = 11,
  pages   = {625-660},
  month   = 2,
}

%  replacement for above
@article{sun2019unsupervised,
  title={Unsupervised domain adaptation through self-supervision},
  author={Sun, Yu and Tzeng, Eric and Darrell, Trevor and Efros, Alexei A},
  journal={arXiv preprint arXiv:1909.11825},
  year={2019}
}


%colorization
@inproceedings{zhang2016colorful,
  title={Colorful Image Colorization},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A},
  booktitle={ECCV},
  year={2016}
}

% Radiology turnaround time
@article{morgan2008radiology,
  title={The radiology digital dashboard: effects on report turnaround time},
  author={Morgan, Matthew B and Branstetter, Barton F and Lionetti, David M and Richardson, Jeremy S and Chang, Paul J},
  journal={Journal of digital imaging},
  volume={21},
  number={1},
  pages={50--58},
  year={2008},
  publisher={Springer}
}

% Survey of semi-supervised learning
@article{sheikhpour2017survey,
  title={A survey on semi-supervised feature selection methods},
  author={Sheikhpour, Razieh and Sarram, Mehdi Agha and Gharaghani, Sajjad and Chahooki, Mohammad Ali Zare},
  journal={Pattern Recognition},
  volume={64},
  pages={141--158},
  year={2017},
  publisher={Elsevier}
}

% Pseudo-labeling
@inproceedings{lee2013pseudo,
  title={Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks},
  author={Lee, Dong-Hyun},
  booktitle={Workshop on challenges in representation learning, ICML},
  volume={3},
  number={2},
  year={2013}
}

% Survery of transfer learning
@inproceedings{tan2018survey,
  title={A survey on deep transfer learning},
  author={Tan, Chuanqi and Sun, Fuchun and Kong, Tao and Zhang, Wenchang and Yang, Chao and Liu, Chunfang},
  booktitle={International conference on artificial neural networks},
  pages={270--279},
  year={2018},
  organization={Springer}
}

% Contrast induced network
@inproceedings{liu2019align,
  title={Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision},
  author={Liu, Jingyu and Zhao, Gangming and Fei, Yu and Zhang, Ming and Wang, Yizhou and Yu, Yizhou},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={10632--10641},
  year={2019}
}

@misc{chaitanya2020contrastive,
      title={Contrastive learning of global and local features for medical image segmentation with limited annotations}, 
      author={Krishna Chaitanya and Ertunc Erdil and Neerav Karani and Ender Konukoglu},
      year={2020},
      eprint={2006.10511},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article {Holmberg861757,
	author = {Holmberg, Olle G. and K{\"o}hler, Niklas D. and Martins, Thiago and Siedlecki, Jakob and Herold, Tina and Keidel, Leonie and Asani, Ben and Schiefelbein, Johannes and Priglinger, Siegfried and Kortuem, Karsten U. and Theis, Fabian J.},
	title = {Self-supervised retinal thickness prediction enables deep learning from unlabeled data to boost classification of diabetic retinopathy},
	elocation-id = {861757},
	year = {2019},
	doi = {10.1101/861757},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2019/12/02/861757},
	eprint = {https://www.biorxiv.org/content/early/2019/12/02/861757.full.pdf},
	journal = {bioRxiv}
}

@article{medicalrubiks,
  author    = {Xinrui Zhuang and
               Yuexiang Li and
               Yifan Hu and
               Kai Ma and
               Yujiu Yang and
               Yefeng Zheng},
  title     = {Self-supervised Feature Learning for 3D Medical Images by Playing
               a Rubik's Cube},
  journal   = {CoRR},
  volume    = {abs/1910.02241},
  year      = {2019},
  url       = {http://arxiv.org/abs/1910.02241},
  archivePrefix = {arXiv},
  eprint    = {1910.02241},
  timestamp = {Wed, 09 Oct 2019 14:07:58 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-02241.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{ZHU2020101746,
title = "Rubik’s Cube+: A self-supervised feature learning framework for 3D medical image analysis",
journal = "Medical Image Analysis",
volume = "64",
pages = "101746",
year = "2020",
issn = "1361-8415",
doi = "https://doi.org/10.1016/j.media.2020.101746",
url = "http://www.sciencedirect.com/science/article/pii/S1361841520301109",
author = "Jiuwen Zhu and Yuexiang Li and Yifan Hu and Kai Ma and S. Kevin Zhou and Yefeng Zheng",
keywords = "Self-supervised learning, 3D Medical imaging data, Rubik’s cube recovery"
}

@article{bai2019cardiac,
  author    = {Wenjia Bai and
               Chen Chen and
               Giacomo Tarroni and
               Jinming Duan and
               Florian Guitton and
               Steffen E. Petersen and
               Yike Guo and
               Paul M. Matthews and
               Daniel Rueckert},
  title     = {Self-Supervised Learning for Cardiac {MR} Image Segmentation by Anatomical
               Position Prediction},
  journal   = {CoRR},
  volume    = {abs/1907.02757},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.02757},
  archivePrefix = {arXiv},
  eprint    = {1907.02757},
  timestamp = {Fri, 27 Dec 2019 21:15:09 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-02757.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{spitzer2019cytoarch,
  author    = {Hannah Spitzer and
               Kai Kiwitz and
               Katrin Amunts and
               Stefan Harmeling and
               Timo Dickscheid},
  title     = {Improving Cytoarchitectonic Segmentation of Human Brain Areas with
               Self-supervised Siamese Networks},
  journal   = {CoRR},
  volume    = {abs/1806.05104},
  year      = {2018},
  url       = {http://arxiv.org/abs/1806.05104},
  archivePrefix = {arXiv},
  eprint    = {1806.05104},
  timestamp = {Mon, 13 Aug 2018 16:48:53 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1806-05104.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{rajpurkar2020chexaid,
  title={CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV},
  author={Rajpurkar, Pranav and O’Connell, Chloe and Schechter, Amit and Asnani, Nishit and Li, Jason and Kiani, Amirhossein and Ball, Robyn L and Mendelson, Marc and Maartens, Gary and van Hoving, Dani{\"e}l J and others},
  journal={NPJ digital medicine},
  volume={3},
  number={1},
  pages={1--8},
  year={2020},
  publisher={Nature Publishing Group}
}

@misc{rajpurkar2021chexternal,
      title={CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings}, 
      author={Pranav Rajpurkar and Anirudh Joshi and Anuj Pareek and Andrew Y. Ng and Matthew P. Lungren},
      year={2021},
      eprint={2102.08660},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

% Model genesis
@inproceedings{zhou2019models,
  title={Models genesis: Generic autodidactic models for 3d medical image analysis},
  author={Zhou, Zongwei and Sodha, Vatsal and Siddiquee, Md Mahfuzur Rahman and Feng, Ruibin and Tajbakhsh, Nima and Gotway, Michael B and Liang, Jianming},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={384--393},
  year={2019},
  organization={Springer}
}



% Semi-supervised chest x-ray
@inproceedings{terzopoulos2019semi,
  title={Semi-supervised multi-task learning with chest x-ray images},
  author={Terzopoulos, Demetri and others},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={151--159},
  year={2019},
  organization={Springer}
}

@article{liao2019semi,
  title={Semi-supervised learning for quantification of pulmonary edema in chest x-ray images},
  author={Liao, Ruizhi and Rubin, Jonathan and Lam, Grace and Berkowitz, Seth and Dalal, Sandeep and Wells, William and Horng, Steven and Golland, Polina},
  journal={arXiv preprint arXiv:1902.10785},
  year={2019}
}

% Pi model (need to use the Greek Pi)
@article{laine2016temporal,
  title={Temporal ensembling for semi-supervised learning},
  author={Laine, Samuli and Aila, Timo},
  journal={arXiv preprint arXiv:1610.02242},
  year={2016}
}

% Good model on CheXpert
@article{pham2020interpreting,
  title={Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels},
  author={Pham, Hieu H and Le, Tung T and Ngo, Dat T and Tran, Dat Q and Nguyen, Ha Q},
  journal={arXiv preprint arXiv:2005.12734},
  year={2020}
}

% model robustness self supervised
@inproceedings{hendrycks2019using,
  title={Using self-supervised learning can improve model robustness and uncertainty},
  author={Hendrycks, Dan and Mazeika, Mantas and Kadavath, Saurav and Song, Dawn},
  booktitle={Advances in Neural Information Processing Systems},
  pages={15663--15674},
  year={2019}
}

% overfitting
@article{ebrahimi2018study,
  title={Study of residual networks for image recognition},
  author={Ebrahimi, Mohammad Sadegh and Abadi, Hossein Karkeh},
  journal={arXiv preprint arXiv:1805.00325},
  year={2018}
}

% other self-supervised stuff
@article{zhou2020comparing,
  title={Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations},
  author={Zhou, Hong-Yu and Yu, Shuang and Bian, Cheng and Hu, Yifan and Ma, Kai and Zheng, Yefeng},
  journal={arXiv preprint arXiv:2007.07423},
  year={2020}
}
