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We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training.
Novelty detection: a review—part 1: statistical approaches
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Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2009
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How to obtain the p value from a confidence interval
Douglas G Altman and J Martin Bland · 2011
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A review of novelty detection
Marco AF Pimentel, David A Clifton, Lei Clifton, and Lionel Tarassenko · 2014
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Learning deep representations of appearance and motion for anomalous event detection
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Abhijit Bendale and Terrance E Boult · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Deep learning for automated classification of tuberculosis-related chest x-ray: Dataset specificity limits diagnostic performance generalizability
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Diederik P Kingma, Viraj Ba, Jimmy Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David Sontag, and Xavier Amatriain · 2019
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Can we trust deep learning models diagnosis? the impact of domain shift in chest radiograph classification
Eduardo HP Pooch, Pedro L Ballester, and Rodrigo C Barros · 2019
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A benchmark of medical out of distribution detection
Tianshi Cao, Chinwei Huang, David Yu-Tung Hui, and Joseph Paul Cohen · 2020
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Recent advances in open set recognition: A survey
Chuanxing Geng, Sheng-jun Huang, and Songcan Chen · 2020
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The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
Gustav Mårtensson, Daniel Ferreira, Tobias Granberg, Lena Cavallin, Ketil Oppedal, Alessandro Padovani, Irena Rektorova, Laura Bonanni, Matteo Pardini, and Milica G Kramberger · 2020
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Ioana Baiu and David Spain · 2019
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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, and Katie Shpanskaya · 2019
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Alistair E. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih ying Deng, Yifan Peng, Zhiyong Lu, Roger G. Mark, Seth J. Berkowitz, and Steven Horng · 2019
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Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Phil Chen, Amirhossein Kiani, Jeremy Irvin, Andrew Y Ng, and Matthew P Lungren · 2020
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Gradient surgery for multi-task learning, 2020
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Consistent estimators for learning to defer to an expert, 2021
Hussein Mozannar and David Sontag · 2021
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