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Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes?, , , , and . ISBI, page 222-225. IEEE, (2015)Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning., , , , and . AMIA, AMIA, (2015)Recognizing patterns of visual field loss using unsupervised machine learning., , , , and . Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 90342M. SPIE, (2014)A joint estimation detection of Glaucoma progression in 3D spectral domain optical coherence tomography optic nerve head images., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9035 of SPIE Proceedings, page 90350O. SPIE, (2014)One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis., , , , , , and . IEEE Trans. Medical Imaging, 42 (12): 3764-3778 (December 2023)Glaucoma progression detection using variational expectation maximization algorithm., , , , and . ISBI, page 876-879. IEEE, (2013)GLANCE: Visual Analytics for Monitoring Glaucoma Progression., , , , , , , , , and 3 other author(s). VCBM, page 85-96. Eurographics Association, (2020)One-Vote Veto: A Self-Training Strategy for Low-Shot Learning of a Task-Invariant Embedding to Diagnose Glaucoma., , , , , , and . CoRR, (2020)Detecting glaucomatous change in visual fields: Analysis with an optimization framework., , , , , , , , , and 1 other author(s). J. Biomed. Informatics, (2015)A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes., , , and . EMBC, page 3869-3872. IEEE, (2014)