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22. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (MIT Press,
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24. C.M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006)
25. C. Bowd, L.M. Zangwill, C.C. Berry, E.Z. Blumenthal, C. Vasile, C. Sanchez-Galeana, C.F.
Bosworth, P.A. Sample, R.N. Weinreb, Detecting early glaucoma by assessment of retinal
nerve fiber layer thickness and visual function. Invest. Ophthalmol. Vis. Sci. 42(9), 1993–2003
(2001)
26. M.T. Leite, H.L. Rao, L.M. Zangwill, R.N. Weinreb, F.A. Medeiros, Comparison of the diagnostic accuracies of the Spectralis, Cirrus, and RTVue optical coherence tomography devices
in glaucoma. Ophthalmology 118(7), 1334–1339 (2011)
27. P.P. Srinivasan, L.A. Kim, P.S. Mettu, S.W. Cousins, G.M. Comer, J.A. Izatt, S. Farsiu, Fully
automated detection of diabetic macular edema and dry age-related macular degeneration from
optical coherence tomography images. Biomed. Opt. Express 5(10), 3568–3577 (2014)
28. G. Lemaître, M. Rastgoo, J. Massich, S. Sankar, F. Mériaudeau, D. Sidibé, Classification of
SD-OCT volumes with LBP: application to DME detection, in OMIA (2015)
29. Y. Wang, Y. Zhang, Z. Yao, R. Zhao, F. Zhou, Machine learning based detection of age-related
macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence
tomography (OCT) images. Biomed. Opt. Express 7(12), 4928–4940 (2016)
30. H. Poon, P. Domingos, Sum-product networks: a new deep architecture, in UAI (2011), pp.
337–346