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Robert N. Weinreb
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2020 – today
- 2023
- [j13]Sonali B. Bhanvadia, Leo Meller
, Kian Madjedi, Robert N. Weinreb
Availability of Physical Activity Tracking Data from Wearable Devices for Glaucoma Patients. Inf. 14(9): 493 (2023) - [j12]Rui Fan
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis. IEEE Trans. Medical Imaging 42(12): 3764-3778 (2023) - 2022
- [j11]Sonali B. Bhanvadia
Assessing Usability of Smartwatch Digital Health Devices for Home Blood Pressure Monitoring among Glaucoma Patients. Informatics 9(4): 79 (2022) - 2021
- [j10]Yongli Xu
A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis. npj Digit. Medicine 4 (2021) - 2020
- [j9]Marcelo Aguilar-Rivera, Dieanira T. Erudaitius, Vincent M. Wu
Smart Electronic Eyedrop Bottle for Unobtrusive Monitoring of Glaucoma Medication Adherence. Sensors 20(9): 2570 (2020) - [j8]Alex Phan
A Wireless Handheld Pressure Measurement System for In Vivo Monitoring of Intraocular Pressure in Rabbits. IEEE Trans. Biomed. Eng. 67(3): 931-937 (2020) - [c5]Astrid van den Brandt
GLANCE: Visual Analytics for Monitoring Glaucoma Progression. VCBM 2020: 85-96 - [i1]Rui Fan
One-Vote Veto: A Self-Training Strategy for Low-Shot Learning of a Task-Invariant Embedding to Diagnose Glaucoma. CoRR abs/2012.04841 (2020)
2010 – 2019
- 2015
- [j7]Akram Belghith, Christopher Bowd
Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression. Artif. Intell. Medicine 64(2): 105-115 (2015) - [j6]Siamak Yousefi
Detecting glaucomatous change in visual fields: Analysis with an optimization framework. J. Biomed. Informatics 58: 96-103 (2015) - [c4]Akram Belghith, Christopher Bowd
Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes? ISBI 2015: 222-225 - 2014
- [j5]Akram Belghith, Madhusudhanan Balasubramanian, Christopher Bowd
A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images. Comput. Medical Imaging Graph. 38(5): 411-420 (2014) - [j4]Siamak Yousefi, Michael H. Goldbaum, Madhusudhanan Balasubramanian, Tzyy-Ping Jung
Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points. IEEE Trans. Biomed. Eng. 61(4): 1143-1154 (2014) - [j3]Siamak Yousefi, Michael H. Goldbaum, Madhusudhanan Balasubramanian, Felipe A. Medeiros, Linda M. Zangwill, Jeffrey M. Liebmann, Christopher A. Girkin
Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements. IEEE Trans. Biomed. Eng. 61(7): 2112-2124 (2014) - [c3]Akram Belghith, Christopher Bowd
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. EMBC 2014: 3869-3872 - [c2]Akram Belghith, Christopher Bowd
A joint estimation detection of Glaucoma progression in 3D spectral domain optical coherence tomography optic nerve head images. Medical Imaging: Computer-Aided Diagnosis 2014: 90350O - 2013
- [c1]Akram Belghith, Madhusudhanan Balasubramanian, Christopher Bowd
Glaucoma progression detection using variational expectation maximization algorithm. ISBI 2013: 876-879
2000 – 2009
- 2007
- [j2]James D. Lindsey, Miriam Scadeng
Magnetic resonance imaging of the visual system in vivo: Transsynaptic illumination of V1 and V2 visual cortex. NeuroImage 34(4): 1619-1626 (2007) - 2002
- [j1]Kwokleung Chan, Te-Won Lee, Pamela A. Sample, Michael H. Goldbaum, Robert N. Weinreb, Terrence J. Sejnowski:
Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans. Biomed. Eng. 49(9): 963-974 (2002)
Coauthor Index
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