1. M.D. Abràmoff, M.K. Garvin, M. Sonka, Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)
2. P. Alonso, A comparison between some discriminative and generative classifiers (logistic regression, support vector machines, neural networks, Naive Bayes and Bayesian networks). Master Thesis, University of Helsinki (2015)
3. R. Arora, A. Basu, P. Mianjy, A. Mukherjee, Understanding deep neural networks with rectified linear units. arXiv preprint arXiv:1611.01491 (2016)
4. C. Becker, R. Rigamonti, V. Lepetit, P. Fua, Supervised feature learning for curvilinear structure segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, 2013), pp. 526–533
5. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel, InfoGAN: interpretable representation learning by information maximizing generative adversarial nets, in Advances in Neural Information Processing Systems, pp. 2172–2180 (2016)