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The use of deep learning for medical imaging has seen tremendous growth in the research community.
Meta-evaluation of image segmentation using machine learning
H. Zhang, S. Cholleti, S. A. Goldman, and J. E. Fritts · 2006
Earlier work this paper cites.
Evaluating segmentation error without ground truth
T. Kohlberger, V. Singh, C. Alvino, C. Bahlmann, and L. Grady · 2012
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Bayesian learning for neural networks
R. M. Neal · 2012
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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A. Kendall, V. Badrinarayanan, and R. Cipolla · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2016
Cited alongside, same era.
Qualitynet: Segmentation quality evaluation with deep convolutional networks
C. Huang, Q. Wu, and F. Meng · 2016
Cited alongside, same era.
Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks
M. Kampffmeyer, A.-B. Salberg, and R. Jenssen · 2016
Cited alongside, same era.
Semantic segmentation using adversarial networks
P. Luc, C. Couprie, S. Chintala, and J. Verbeek · 2016
Cited alongside, same era.
Fast predictive image registration
X. Yang, R. Kwitt, and M. Niethammer · 2016
Cited alongside, same era.
Leveraging uncertainty information from deep neural networks for disease detection
C. Leibig, V. Allken, M. S. Ayhan, P. Berens, and S. Wahl · 2017
Later among the works it cites.
A survey on deep learning in medical image analysis
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. van Ginneken, and C. I. Sánchez · 2017
Later among the works it cites.
Detecting cancer metastases on gigapixel pathology images
Y. Liu, K. Gadepalli, M. Norouzi, G. E. Dahl, T. Kohlberger, A. Boyko, S. Venugopalan, A. Timofeev, P. Q. Nelson, G. S. Corrado, J. D. Hipp, L. Peng, and M. C. Stumpe · 2017
Later among the works it cites.
Multiplicative normalizing flows for variational bayesian neural networks
C. Louizos and M. Welling · 2017
Later among the works it cites.
Deep generative adversarial networks for compressed sensing automates MRI
M. Mardani, E. Gong, J. Y. Cheng, S. Vasanawala, G. Zaharchuk, M. Alley, N. Thakur, S. Han, W. Dally, J. M. Pauly, and L. Xing · 2017
Later among the works it cites.
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N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, et al · 2017
Cited alongside, same era.
Dermatologist-level classification of skin cancer with deep neural networks
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
Cited alongside, same era.
Adversarial networks for the detection of aggressive prostate cancer
S. Kohl, D. Bonekamp, H.-P. Schlemmer, K. Yaqubi, M. Hohenfellner, B. Hadaschik, J.-P. Radtke, and K. Maier-Hein · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Cited alongside, same era.
Adversarial training and dilated convolutions for brain mri segmentation
P. Moeskops, M. Veta, M. W. Lafarge, K. A. Eppenhof, and J. P. Pluim · 2017
Later among the works it cites.
Reverse classification accuracy: predicting segmentation performance in the absence of ground truth
V. V. Valindria, I. Lavdas, W. Bai, K. Kamnitsas, E. O. Aboagye, A. G. Rockall, D. Rueckert, and B. Glocker · 2017
Later among the works it cites.
Uncertainty estimation via stochastic batch normalization
A. Atanov, A. Ashukha, D. Molchanov, K. Neklyudov, and D. Vetrov · 2018
Closest in time.
Learning confidence for out-of-distribution detection in neural networks
T. DeVries and G. W. Taylor · 2018
Closest in time.