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Deep learning-based segmentation methods are vulnerable to unforeseen data distribution shifts during deployment, e.g.
The information bottleneck method
Naftali Tishby et al · 2000
Earlier work this paper cites.
Efficient object localization using convolutional networks
Jonathan Tompson et al · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow et al · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger et al · 2015
Earlier work this paper cites.
Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields
Patrick Ferdinand Christ et al · 2016
Earlier work this paper cites.
Deep learning in medical image analysis
Dinggang Shen et al · 2017
Earlier work this paper cites.
A survey on deep learning in medical image analysis
Geert Litjens et al · 2017
Earlier work this paper cites.
Thinking, fast and slow, 2017
Kahneman Daniel · 2017
Earlier work this paper cites.
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Terrance Devries et al · 2017
Earlier work this paper cites.
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Yu Zhang et al · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
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Earlier work this paper cites.
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