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Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks.

, , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 1094931. SPIE, (2019)

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Convolutional neural network-based regression for biomarker estimation in corneal endothelium microscopy images., , , , and . EMBC, page 876-881. IEEE, (2019)Corneal Endothelial Cell Segmentation by Classifier-Driven Merging of Oversegmented Images., , , , , , and . IEEE Trans. Med. Imaging, 37 (10): 2278-2289 (2018)AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge., , , , , , , , , and 26 other author(s). CoRR, (2023)Automatic estimation of retinal nerve fiber bundle orientation in SD-OCT images using a structure-oriented smoothing filter., , , , and . Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101330C. SPIE, (2017)Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy., , , , and . BMC Medical Imaging, (2015)Noise-adaptive attenuation coefficient estimation in spectral domain optical coherence tomography data., , , , and . ISBI, page 706-709. IEEE, (2016)Glaucoma Assessment from OCT images using Capsule Network., , , and . EMBC, page 5581-5584. IEEE, (2019)Loosely coupled level sets for retinal layers and drusen segmentation in subjects with dry age-related macular degeneration., , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97842P. SPIE, (2016)Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy., , , , , and . ISBI, page 702-705. IEEE, (2016)Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks., , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 1094931. SPIE, (2019)