Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc

Gagnon, 2001, Procedure to detect anatomical structures in optical fundus images, 1218

Larsen, 1976

Schwiegerling, 2004

Hoover, 2003, Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels Medical Imaging, IEEE Trans. on, 22, 951

Foracchia, 2004, Detection of optic disc in retinal images by means of a geometrical model of vessel structure Medical Imaging, IEEE Trans. on, 23, 1189

Fleming, 2006, Automatic detection of retinal anatomy to assist diabetic retinopathy screening, Phys. Med. Biol., 52, 331, 10.1088/0031-9155/52/2/002

Tobin, 2007, Detection of anatomic structures in human retinal imagery Medical Imaging, IEEE Trans. on, 26, 1729

Youssif, 2008, Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter Medical Imaging, IEEE Trans. on, 27, 11

Niemeijer, 2009, Fast detection of the optic disc and fovea in color fundus photographs, Med. Image Anal., 13, 859, 10.1016/j.media.2009.08.003

Zhu, 2010, Detection of the optic nerve head in fundus images of the retina using the Hough transform for circles, J. Digit. Imaging, 23, 332, 10.1007/s10278-009-9189-5

Lu, 2011, Accurate and efficient optic disc detection and segmentation by a circular transformation, Med. Imaging, IEEE Trans. on, 30, 2126, 10.1109/TMI.2011.2164261

Yu, 2012, Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets Information Technology in Biomedicine, IEEE Transactions on, 16, 644

Dehghani, 2012, Optic disc localization in retinal images using histogram matching, EURASIP J. Image Video Process., 2012, 1, 10.1186/1687-5281-2012-19

Harangi, 2015, Detection of the optic disc in fundus images by combining probability models, Comput. Biol. Med., 65, 10, 10.1016/j.compbiomed.2015.07.002

Calimeri, 2016, Optic disc detection using fine tuned convolutional neural networks, Signal-Image Technology & Internet-Based Systems (SITIS), 2016 12th International Conference on IEEE, 69, 10.1109/SITIS.2016.20

Li, 2004, Automated feature extraction in color retinal images by a model based approach Biomedical Engineering, IEEE Trans. on, 51, 246

Niemeijer, 2007, Segmentation of the optic disc, macula and vascular arch in fundus photographs Medical Imaging, IEEE Trans. on, 26, 116

Welfer, 2011, Fovea center detection based on the retina anatomy and mathematical morphology, Comput. Methods Programs Biomed., 104, 397, 10.1016/j.cmpb.2010.07.006

Qureshi, 2012, Combining algorithms for automatic detection of optic disc and macula in fundus images, Comput. Vision Image Understanding, 116, 138, 10.1016/j.cviu.2011.09.001

Giachetti, 2013, The use of radial symmetry to localize retinal landmarks, Comput. Med. Imaging Graph., 37, 369, 10.1016/j.compmedimag.2013.06.005

Gegundez-Arias, 2013, Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques, Comput. Med. Imaging Graph., 37, 386, 10.1016/j.compmedimag.2013.06.002

Aquino, 2014, Establishing the macular grading grid by means of fovea center detection using anatomical-based and visual-based features, Comput. Biol. Med., 55, 61, 10.1016/j.compbiomed.2014.10.007

Decenciere, 2014, Feedback on a publicly distributed image database: the Messidor database, Image Analysis and Stereology, 231, 10.5566/ias.1155

2016

Zuiderveld, 1994, Contrast limited adaptive histogram equalization, 474

Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097

Girshick, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580

Long, 2014, Do convnets learn correspondence?, 1607

Ciresan, 2012, Deep neural networks segment neuronal membranes in electron microscopy images, 2843

Cernazanu-Glavan, 2013, Segmentation of bone structure in X-ray images using convolutional neural network, Adv. Electr. Comput Eng, 13, 87, 10.4316/AECE.2013.01015

Li, 2014, 3d human pose estimation from monocular images with deep convolutional neural network, 332

Levi, 2015, Age and gender classification using convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 34

LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791

Ranzato, 2007, Unsupervised learning of invariant feature hierarchies with applications to object recognition, Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, 1

Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, 15, 1929

Glorot, 2011, Deep sparse rectifier neural networks, In International Conference on Artificial Intelligence and Statistics

Lasagne/Lasagne, Retrieved from https://github.com/Lasagne/Lasagne (October 18, 2016)

Theano/Theano. (2016, October 18). Retrieved from https://github.com/Theano/Theano.

Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, International Conference on Artificial Intelligence and Statistics, 249

Hoover, 2000, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response Medical Imaging, IEEE Transactions on, 19, 203

Staal, 2004, Ridge-based vessel segmentation in color images of the retina Medical Imaging, IEEE Trans. on, 23, 501

Zheng, 2012, Automated disease/no disease grading of age-related macular degeneration by an image mining approach, Invest. Ophthalmol. Visual Sci., 53, 8310, 10.1167/iovs.12-9576

Kälviäinen, 2007, DIARETDB1 diabetic retinopathy database and evaluation protocol, Med. Image Unders. Anal., 2007, 61

Kauppi, 2006, DIARETD, B0

Snoek, 2015

Hinton, 2012, Neural networks: tricks of the trade, 599, 10.1007/978-3-642-35289-8_32