M. Goldbaum

发表

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

A.D. Hoover, V. Kouznetsova, M. Goldbaum, 2000, IEEE Transactions on Medical Imaging.

Detection of blood vessels in retinal images using two-dimensional matched filters.

M. Goldbaum, S. Chaudhuri, S. Chatterjee, 1989, IEEE transactions on medical imaging.

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Daniel S. Kermany, Sally L. Baxter, M. Goldbaum, 2018, Cell.

Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.

T. Sejnowski, Te-Won Lee, M. Goldbaum, 2005, Investigative ophthalmology & visual science.

Machine Learning Classifiers Can Detect Subtle Field Defects in Eyes of HIV Subjects

Terrence J. Sejnowski, Jiucang Hao, T.–W. Lee, 2007 .

Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers.

T. Sejnowski, Te-Won Lee, M. Goldbaum, 2004, Investigative ophthalmology & visual science.

Pattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART

M. Goldbaum, W. Freeman, I. Grant, 2010, Graefe's Archive for Clinical and Experimental Ophthalmology.

Paring Neural Networks and Linear Discriminant Functions for Glaucoma

T. Sejnowski, Te-Won Lee, M. Goldbaum, 2005 .

Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.

Te-Won Lee, M. Goldbaum, F. Medeiros, 2008, Investigative ophthalmology & visual science.

Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

T. Sejnowski, Te-Won Lee, M. Goldbaum, 2005, Investigative ophthalmology & visual science.

Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.

Robert N Weinreb, Christopher Bowd, Intae Lee, 2012, Investigative ophthalmology & visual science.

Machine learning classifiers detect subtle field defects in eyes of HIV individuals.

Robert N Weinreb, Jiucang Hao, Te-Won Lee, 2007, Transactions of the American Ophthalmological Society.

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

Robert N Weinreb, Jiucang Hao, Gil-Jin Jang, 2009, Transactions of the American Ophthalmological Society.

Gradient Bosting Classifiers Combining Vessel Density and Tissue Thickness Measurements for Classifying Early to Moderate Glaucoma.

Mark A. Christopher, M. Goldbaum, L. Zangwill, 2020, American journal of ophthalmology.

Retinal vein occlusion is associated with stroke independent of underlying cardiovascular disease

M. Goldbaum, W. Freeman, A. DeMaria, 2022, Eye.

The extracellular matrix of the human optic nerve.

M. Goldbaum, R. Weinreb, S. Jeng, 1989, Archives of ophthalmology.

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2018, Investigative ophthalmology & visual science.

PREVALENCE OF MISMATCH REPAIR GENE MUTATIONS IN UVEAL MELANOMA.

Bobby S. Korn, M. Goldbaum, L. Ediriwickrema, 2020, Retina.

Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification

Michael H. Goldbaum, Kang Zhang, Daniel S. Kermany, 2018 .

Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images

Michael H. Goldbaum, Kang Zhang, Daniel S. Kermany, 2018 .

Predicting glaucoma prior to its onset using deep learning

M. Goldbaum, Siamak Yousefi, Anshul Thakur, 2019 .

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from Optical Coherence Tomography Optic Nerve Head Enface Images and Retinal Nerve Fiber Layer Thickness Maps

Weinreb, Massimo A. Fazio, M. Goldbaum, 2021 .

Retinal Ischemic Perivascular Lesions, a biomarker of cardiovascular disease.

M. Goldbaum, A. DeMaria, D. Sarraf, 2022, Ophthalmology. Retina.

Detecting Glaucoma in the Ocular Hypertension Treatment Study Using Deep Learning: Implications for clinical trial endpoints

D. Kriegman, M. Goldbaum, M. Kass, 2021 .

TELEVISION OPHTHALMOSCOPE IMAGE PROCESSOR.

Michael H. Goldbaum, Bruce H. McCormick, Morton F. Goldberg, 1977 .

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization

M. Pazzani, D. Kriegman, M. Goldbaum, 2022, Ophthalmology science.

Expanding access to knowledge and information.

M. Goldbaum, 1987, Archives of ophthalmology.

GNAQ and PMS1 Mutations Associated with Uveal Melanoma, Ocular Surface Melanosis, and Nevus of Ota

M. Goldbaum, Jonathan H. Lin, J. Thorson, 2019, Ocular Oncology and Pathology.

Geometric analysis of radial buckling.

M. Goldbaum, H. Lincoff, M. Smithline, 1975, American journal of ophthalmology.

Genotypic Analysis Of Treatment Response To Anti-vegf Therapy In Age-related Macular Degeneration

M. Goldbaum, W. Freeman, I. Kozák, 2011 .

Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2014, IEEE Transactions on Biomedical Engineering.

Comparison of machine learning and traditional classifiers in glaucoma diagnosis

Terrence J. Sejnowski, Pamela A. Sample, Robert N. Weinreb, 2002, IEEE Transactions on Biomedical Engineering.

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

Robert N Weinreb, Christopher Bowd, Te-Won Lee, 2002, Investigative ophthalmology & visual science.

Clinical Outcomes Comparison of Combined Small Incision Lenticule Extraction with Collagen Cross-Linking Versus Small Incision Lenticule Extraction Only

M. Goldbaum, N. Afshari, M. Fantozzi, 2022, Journal of ophthalmology.

Symposium on Medical and Surgical Diseases of the Retina and Vitreous: Transactions of the New Orleans Academy of Ophthalmology

M. Goldbaum, 1984 .

Foveal hypoplasia demonstrated in vivo with optical coherence tomography.

M. Goldbaum, R. Weinreb, D. McGuire, 2003, American journal of ophthalmology.

Silicone oil tamponade to seal macular holes without position restrictions.

H. H. Chen, M. Goldbaum, A. Hanneken, 1998, Ophthalmology.

A modified COMS plaque for iris melanoma

M. Goldbaum, A. Mundt, R. Rice, 2011, Journal of contemporary brachytherapy.

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

Xin Sun, Wei Yu, Long Zhu, 2019, Nature Medicine.

BAP1 methylation: a prognostic marker of uveal melanoma metastasis

M. Goldbaum, P. Mischel, M. Bakhoum, 2021, npj Precision Oncology.

PARASITE IN A YOUNG GIRL FROM VIETNAM PRESENTING AS A GOLDEN FOVEAL LESION: Submacular Fly Larva or Nematode?

M. Goldbaum, Payam Amini, A. Khatibi, 2013, Retinal cases & brief reports.

Interpretation of automated perimetry for glaucoma by neural network.

M H Goldbaum, P A Sample, H White, 1994, Investigative ophthalmology & visual science.

Visual phenomena perceived during pars plana vitrectomy under peribulbar block and monitored anaesthesia care

Cheryl A. Arcinue, M. Goldbaum, W. Freeman, 2015, British Journal of Ophthalmology.

Loss of polycomb repressive complex 1 activity and chromosomal instability drive uveal melanoma progression

Ashley M. Laughney, M. Goldbaum, P. Mischel, 2021, Nature Communications.

Analysis with support vector machine shows HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyes.

Jiucang Hao, Michael H Goldbaum, Dirk-Uwe Bartsch, 2008, Transactions of the American Ophthalmological Society.

Prevalence of subclinical retinal ischemia in patients with cardiovascular disease – a hypothesis driven study

Alison X. Chan, Anupam K. Garg, M. Goldbaum, 2021, EClinicalMedicine.

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2016, Translational vision science & technology.

Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects.

Michael Henry Goldbaum, M. Goldbaum, 2006, Transactions of the American Ophthalmological Society.

Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

Robert N Weinreb, Christopher Bowd, Jiucang Hao, 2005, Investigative ophthalmology & visual science.

Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

D. Kriegman, M. Goldbaum, M. Kass, 2022, JAMA ophthalmology.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms

Christopher Bowd, Linda M. Zangwill, Robert N. Weinreb, 2020, Translational vision science & technology.

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Robert N Weinreb, Christopher Bowd, Mark Christopher, 2019, Ophthalmology.

Recognizing patterns of visual field loss using unsupervised machine learning

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2014, Medical Imaging.

Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2014, PloS one.

Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects.

Robert N Weinreb, Jiucang Hao, Te-Won Lee, 2004, Investigative ophthalmology & visual science.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2015, J. Biomed. Informatics.

Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points

Tzyy-Ping Jung, Christopher Bowd, Linda M. Zangwill, 2014, IEEE Transactions on Biomedical Engineering.

Glaucoma Precognition Based on Confocal Scanning Laser Ophthalmoscopy Images of the Optic Disc Using Convolutional Neural Network

Siamak Yousefi, Krati Gupta, Michael Goldbaum, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Luminosity and contrast normalization in color retinal images based on standard reference image

Michael H. Goldbaum, Siamak Yousefi, Ehsan Shahrian, 2016, SPIE Medical Imaging.

Permanent postoperative vision loss associated with expansion of intraocular gas in the presence of a nitrous oxide-containing anesthetic.

M. Goldbaum, W. Freeman, G. Manecke, 2002, Anesthesiology.

DYNAMICS OF THE MACULAR HOLE-SILICONE OIL TAMPONADE INTERFACE WITH PATIENT POSITIONING AS IMAGED BY SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY

M. Goldbaum, W. Freeman, D. Bartsch, 2010, Retina.

Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels

Michael H. Goldbaum, Adam W. Hoover, M. Goldbaum, 2003, IEEE Transactions on Medical Imaging.

Optic nerve head problem.

M. Goldbaum, C. Shields, Kevin C. Chen, 2017, Survey of ophthalmology.

Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images

Jeffrey E. Boyd, Ramesh C. Jain, Michael H. Goldbaum, 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

Predicting glaucoma prior to its onset using deep learning

Siamak Yousefi, Anshul Thakur, Michael Goldbaum, 2019, bioRxiv.

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

Daniel S. Kermany, Sally L. Baxter, M. Goldbaum, 2019, Nature Medicine.

Ophthalmic manifestations of tuberous sclerosis: a review

M. Goldbaum, M. Kinori, S. Robbins, 2017, Clinical & experimental ophthalmology.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

Christopher Bowd, Linda M. Zangwill, Robert N. Weinreb, 2018, Scientific Reports.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

Mark A. Christopher, M. Goldbaum, L. Zangwill, 2018, Scientific Reports.

Measurement and classification of retinal vascular tortuosity

Michael H. Goldbaum, William E. Hart, Paul Kube, 1999, Int. J. Medical Informatics.

Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc.

Robert N Weinreb, Christopher Bowd, Te-Won Lee, 2002, Investigative ophthalmology & visual science.

Digital Image Processing for Ophthalmology

Michael H. Goldbaum, Subhasis Chaudhuri, Shankar Chatterjee, 1990 .

Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.

Robert N Weinreb, Jiucang Hao, Kwokleung Chan, 2007, Investigative ophthalmology & visual science.

Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response

A Hoover, V Kouznetsova, M Goldbaum, 1998, AMIA.

Automated measurement of retinal vascular tortuosity

Michael H. Goldbaum, William E. Hart, Paul Kube, 1997, AMIA.

Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.

Tzyy-Ping Jung, Robert N Weinreb, Christopher Bowd, 2012, Investigative ophthalmology & visual science.

Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics based on Thickness Maps from Macula Optical Coherence Tomography.

M. Goldbaum, L. Zangwill, R. Weinreb, 2021, Ophthalmology.

Vessels segmentation in color retinal images using ensemble of bagged decision trees and patched based principle component analysis and linear discriminant analysis

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2015 .

Assessing susceptibility to age-related macular degeneration with genetic markers and environmental factors.

M. Goldbaum, Kang Zhang, W. Freeman, 2011, Archives of ophthalmology.

Convex Representations Using Deep Archetypal Analysis for Predicting Glaucoma

Michael H. Goldbaum, Siamak Yousefi, Anshul Thakur, 2020, IEEE Journal of Translational Engineering in Health and Medicine.

Validation of the prognostic utility of the gene expression profiling test in patients with uveal melanoma.

Alison X. Chan, M. Goldbaum, M. Sznol, 2023, Ophthalmology.

Registering retinal images using automatically selected control point pairs

Michael H. Goldbaum, William E. Hart, William E. Hart, 1994, Proceedings of 1st International Conference on Image Processing.

Automated registration of digital ocular fundus images for comparison of lesions

Mark Nelson, Michael H. Goldbaum, William E. Hart, 1993, Photonics West - Lasers and Applications in Science and Engineering.

The discrimination of similarly colored objects in computer images of the ocular fundus.

M H Goldbaum, L. R. Haff, N P Katz, 1990, Investigative ophthalmology & visual science.

Improved diabetic control and retinopathy.

M. Goldbaum, M. Lorenzi, C. Cheney, 1983, The New England journal of medicine.

A Bayesian network based sequential inference for diagnosis of diseases from retinal images

Suman K. Mitra, Michael H. Goldbaum, Te-Won Lee, 2005, Pattern Recognit. Lett..

Cryotherapy of proliferative sickle retinopathy, II: triple freeze-thaw cycle.

M. Goldbaum, M. Goldbaum, M. Goldberg, 1979, The British journal of ophthalmology.

Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes.

Robert N Weinreb, Christopher Bowd, Jiucang Hao, 2004, Investigative ophthalmology & visual science.

Refractive Changes After Implantation of Reversed Intraocular Lens in Cataract Surgery: A Mathematical Model.

M. Goldbaum, N. Afshari, Hedieh Matinrad, 2023, Journal of refractive surgery.

Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning

Christopher Bowd, Michael H. Goldbaum, Siamak Yousefi, 2015, AMIA.

Late microhyphema associated with a cataract incision.

M. Goldbaum, R. Weinreb, L. Siegel, 1997, A M A Archives of Ophthalmology.

Peripheral proliferative retinopathies: an update on angiogenesis, etiologies and management.

M. Goldbaum, M. Goldbaum, L. Jampol, 1994, Survey of ophthalmology.

Endoresection of Irradiated Choroidal Melanoma as a Treatment for Intractable Vitreous Hemorrhage and Secondary Blood-Induced Glaucoma

M. Goldbaum, R. Weinreb, H. Ferreyra, 2008, Seminars in ophthalmology.

Beneficial effects of intensive therapy of diabetes during adolescence: outcomes after the conclusion of the Diabetes Control and Complications Trial (DCCT).

P A Cleary, M. Goldbaum, S. Schwartz, 2001, The Journal of pediatrics.

Comparison of conventional color fundus photography and multicolor imaging in choroidal or retinal lesions

William R. Freeman, Dirk-Uwe Bartsch, Amit Meshi, 2018, Graefe's Archive for Clinical and Experimental Ophthalmology.

Combining Functional and Structural Tests Improves the Diagnostic Accuracy of Relevance Vector Machine Classifiers

Robert N Weinreb, Christopher Bowd, Jiucang Hao, 2010, Journal of glaucoma.

Imaging the microvasculature of choroidal melanomas with confocal indocyanine green scanning laser ophthalmoscopy.

M. Goldbaum, H. Boldt, L. M. Gardner, 1998, Archives of ophthalmology.

Comparison of machine learning and traditional classifiers in glaucoma diagnosis

T. Sejnowski, Te-Won Lee, M. Goldbaum, 2002, IEEE Transactions on Biomedical Engineering.

Evolution of the retinal black sunburst in sickling haemoglobinopathies.

M. Goldbaum, M. Goldberg, M. Rabb, 1975, The British journal of ophthalmology.

Deep learning image analysis of optical coherence tomography angiography measured vessel density improves classification of healthy and glaucoma eyes.

M. Goldbaum, L. Zangwill, R. Weinreb, 2021, American journal of ophthalmology.

Machine Learning Classifiers in Glaucoma

Christopher Bowd, Michael H Goldbaum, M. Goldbaum, 2008, Optometry and vision science : official publication of the American Academy of Optometry.

Endoresection of Irradiated Choroidal Melanoma as a Treatment for Intractable Vitreous Hemorrhage and Secondary Blood-Induced Glaucoma

M. Goldbaum, R. Weinreb, H. Ferreyra, 2008, Seminars in ophthalmology.

Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields.

Robert N Weinreb, Chris A Johnson, Te-Won Lee, 2002, Investigative ophthalmology & visual science.

Glance: A Visual Analytics Approach for Opening the Black Box to Explain Deep Learning Predictions of Glaucomatous Visual Field Damage from Optical Coherence Tomography Scans

Christopher Bowd, Linda M. Zangwill, Robert N. Weinreb, 2020 .

Retinal Ischemic Perivascular Lesions in Individuals With Atrial Fibrillation

Alison X. Chan, M. Goldbaum, A. DeMaria, 2023, Journal of the American Heart Association.

Longitudinal time-series of color retinal Fundus Image for Diabetic Retinopathy

Saied Moezzi, Alireza Osareh, Shankar Chatterjee, 2011 .

Image Processing in Television Ophthalmoscopy

Michael H. Goldbaum, B. H. McCormick, R. T. Borovec, 1976 .

Association of LIPC and advanced age-related macular degeneration

M. Goldbaum, Yaou Duan, G. Hughes, 2013, Eye.

DIGITAL OVERLAY OF FLUORESCEIN ANGIOGRAMS AND FUNDUS IMAGES FOR TREATMENT OF SUBRETINAL NEOVASCULARIZATION

M. Goldbaum, T. Clark, W. Freeman, 1992, Retina.

Fuzzy convergence

Michael H. Goldbaum, Adam W. Hoover, M. Goldbaum, 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest

M. Goldbaum, L. Zangwill, R. Weinreb, 2021, Translational vision science & technology.

Combining Functional and Structural Tests Improves the Diagnostic Accuracy of Relevance Vector Machine Classifiers

Te-Won Lee, M. Goldbaum, L. Zangwill, 2010, Journal of glaucoma.