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Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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Deep autoencoding models for unsupervised anomaly segmentation in brain mr images
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Izhak Golan and Ran El-Yaniv · 2018
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Context-encoding variational autoencoder for unsupervised anomaly detection
David Zimmerer, Jens Petersen, Fabian Isensee, and Klaus Maier-Hein · 2019
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Self-supervised learning for medical image analysis using image context restoration
Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, and Daniel Rueckert · 2019
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f-anogan: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth · 2019
Cited alongside, same era.
Unsupervised anomaly localization using variational auto-encoders
David Zimmerer, Fabian Isensee, Jens Petersen, Simon Kohl, and Klaus Maier-Hein · 2019
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High-and low-level image component decomposition using vaes for improved reconstruction and anomaly detection
David Zimmerer, Jens Petersen, and Klaus Maier-Hein · 2019
Later among the works it cites.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Later among the works it cites.
Autoencoders for unsupervised anomaly segmentation in brain mr images: A comparative study
Christoph Baur, Stefan Denner, Benedikt Wiestler, Shadi Albarqouni, and Nassir Navab · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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