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Chapter 2 Deep convolutional neural network in medical image processing  57




                [77] D. Nie, L. Wang, Y. Gao, D. Shen, Fully convolutional networks for multi-
                    modality isointense infant brain image segmentation, in: 2016 IEEE 13th
                    International Symposium on Biomedical Imaging (ISBI), IEEE, 2016, April,
                    pp. 1342e1345.
                [78] M. Shakeri, S. Tsogkas, E. Ferrante, S. Lippe, S. Kadoury, N. Paragios,
                    I. Kokkinos, Sub-cortical brain structure segmentation using F-CNN's, in:
                    2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI),
                    IEEE, 2016, April, pp. 269e272.
                [79] L. Zhao, K. Jia, Multiscale CNNs for brain tumor segmentation and
                    diagnosis, Comput. Math. Methods Med. 2016 (2016), 8356294e8356294,
                    https://doi.org/10.1155/2016/8356294.
                [80] Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V.C. Mok, L. Shi,
                    P.A. Heng, Automatic detection of cerebral microbleeds from MR images
                    via 3D convolutional neural networks, IEEE Trans. Med. Imaging 35 (5)
                    (2016) 1182e1195.
                [81] T. Schlegl, S.M. Waldstein, W.D. Vogl, U. Schmidt-Erfurth, G. Langs,
                    Predicting semantic descriptions from medical images with convolutional
                    neural networks, in: International Conference on Information Processing
                    in Medical Imaging, Springer, Cham, 2015, June, pp. 437e448.
                [82] X. Gao, S. Lin, T.Y. Wong, Automatic feature learning to grade nuclear
                    cataracts based on deep learning, IEEE Trans. Biomed. Eng. 62 (11) (2015)
                    2693e2701.
                [83] X. Chen, Y. Xu, D.W.K. Wong, T.Y. Wong, J. Liu, Glaucoma detection
                    based on deep convolutional neural network, in: 2015 37th Annual
                    International Conference of the IEEE Engineering in Medicine and
                    Biology Society (EMBC), IEEE, 2015, August, pp. 715e718.
                [84] D.E. Worrall, C.M. Wilson, G.J. Brostow, Automated retinopathy of
                    prematurity case detection with convolutional neural networks, in: Deep
                    Learning and Data Labeling for Medical Applications, Springer, Cham,
                    2016, pp. 68e76.
                [85] P. Prenta  si  c, S. Lon  cari  c, Detection of exudates in fundus photographs
                    using deep neural networks and anatomical landmark detection fusion,
                    Comput. Methods Progr. Biomed. 137 (2016) 281e292.
                [86] P. Burlina, D.E. Freund, N. Joshi, Y. Wolfson, N.M. Bressler, Detection of
                    age-related macular degeneration via deep learning, in: 2016 IEEE 13th
                    International Symposium on Biomedical Imaging (ISBI), IEEE, 2016, April,
                    pp. 184e188.
                [87] M.J. Van Grinsven, B. van Ginneken, C.B. Hoyng, T. Theelen, C.I. S  anchez,
                    Fast convolutional neural network training using selective data sampling:
                    application to hemorrhage detection in color fundus images, IEEE Trans.
                    Med. Imaging 35 (5) (2016) 1273e1284.
                [88] J. Zilly, J.M. Buhmann, D. Mahapatra, Glaucoma detection using entropy
                    sampling and ensemble learning for automatic optic cup and disc
                    segmentation, Comput. Med. Imaging Graph. 55 (2017) 28e41.
                [89] D. Mahapatra, P.K. Roy, S. Sedai, R. Garnavi, Retinal image quality
                    classification using saliency maps and CNNs, in: International Workshop
                    on Machine Learning in Medical Imaging, Springer, Cham, 2016, October,
                    pp. 172e179.
                [90] H. Fu, Y. Xu, D.W.K. Wong, J. Liu, Retinal vessel segmentation via deep
                    learning network and fully-connected conditional random fields, in: 2016
                    IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE,
                    2016, April, pp. 698e701.
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