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Self-supervised contrastive learning between pairs of multiple views of the same image has been shown to successfully leverage unlabeled data to produce meaningful visual representations for both natural and medical images.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
Earlier work this paper cites.
Deep learning for the digital pathologic diagnosis of cholangiocarcinoma and hepatocellular carcinoma: Evaluating the impact of a web-based diagnostic assistant, 2019
Bora Uyumazturk, Amirhossein Kiani, Pranav Rajpurkar, Alex Wang, Robyn L. Ball, Rebecca Gao, Yifan Yu, Erik Jones, Curtis P. Langlotz, Brock Martin, Gerald J. Berry, Michael G. Ozawa, Florette K. Hazard, Ryanne A. Brown, Simon B. Chen, Mona Wood, Libby S. Allard, Lourdes Ylagan, Andrew Y. Ng, and Jeanne Shen · 2019
Cited alongside, same era.
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2020
Cited alongside, same era.
Hard negative mixing for contrastive learning, 2020
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
Cited alongside, same era.
Clocs: Contrastive learning of cardiac signals
Dani Kiyasseh, Tingting Zhu, and David A Clifton · 2020
Cited alongside, same era.
Viewmaker networks: Learning views for unsupervised representation learning, 2020
Alex Tamkin, Mike Wu, and Noah Goodman · 2020
Later among the works it cites.
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Later among the works it cites.
Contrastive learning of medical visual representations from paired images and text
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz · 2020
Later among the works it cites.
Big self-supervised models advance medical image classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, et al · 2021
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Covid-19 prognosis via self-supervised representation learning and multi-image prediction
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Appendixnet: Deep learning for diagnosis of appendicitis from a small dataset of ct exams using video pretraining
Pranav Rajpurkar, Allison Park, Jeremy Irvin, Chris Chute, Michael Bereket, Domenico Mastrodicasa, Curtis P Langlotz, Matthew P Lungren, Andrew Y Ng, and Bhavik N Patel · 2020
Cited alongside, same era.
Moco pretraining improves representation and transferability of chest x-ray models
Hari Sowrirajan, Jingbo Yang, Andrew Y Ng, and Pranav Rajpurkar · 2020
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance-level discrimination, 2018a
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin
Cited in the paper.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin
Cited in the paper.
A Sriram, M Muckley, K Sinha, F Shamout, J Pineau, KJ Geras, L Azour, Y Aphinyanaphongs, N Yakubova, and W Moore · 2021
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