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Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern.
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., and Van Ginneken, B · 2004
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Later among the works it cites.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M · 2017
Later among the works it cites.
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