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Huang, Gao, et al. \"Densely connected convolutional networks.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2017."},{"key":"e_1_3_2_1_16_1","volume-title":"Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images.\" arXiv preprint arXiv:1603.04833","author":"Maji Debapriya","year":"2016","unstructured":"Maji , Debapriya , et al. \" Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images.\" arXiv preprint arXiv:1603.04833 ( 2016 ). 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