Michael H. Goldbaum

发表

Relevance Vector Machine for Combining OCT and Standard Automated Perimetry Results for Discriminating Between Healthy and Glaucoma Eyes

Christopher Bowd, Jiucang Hao, Ivan Maynart Tavares, 2006 .

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 .

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.

Early detection of visual field conversion using an unsupervised machine-learning analysis of retinal nerve fiber layer thickness measurements

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2016 .

An Image Processing System For Automatic Retina Diagnosis

Michael H. Goldbaum, Mark E. Nelson, Subhasis Chaudhuri, 1988, Photonics West - Lasers and Applications in Science and Engineering.

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

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

Accurate Retinal Artery and Vein Classification using Local Binary Patterns

Michael H. Goldbaum, Nima Hatami, 2014 .

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.

Automated measurement of retinal vascular tortuosity

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

Image Processing in Television Ophthalmoscopy

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

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.

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

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

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 .

Deep Learning Models Predict Visual Function from Macula Thickness Map

Christopher Bowd, Linda M. Zangwill, Robert N. Weinreb, 2019 .

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.

Content-based retrieval of ophthalmological images

Michael H. Goldbaum, Saied Moezzi, Amarnath Gupta, 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

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.

Recognizing glaucomatous defect patterns and detecting progression from visual field measurements using Gaussian mixture model and expectation maximization

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

Image Database Assisted Classification

Marcel Worring, Simone Santini, Michael H. Goldbaum, 1999, VISUAL.

Digital Image Processing for Ophthalmology

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

Measurement and classification of retinal vascular tortuosity

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

TELEVISION OPHTHALMOSCOPE IMAGE PROCESSOR.

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

Learning Manifolds Transformation for Classification of Standard Automated Perimetry

Jiucang Hao, Linda M. Zangwill, T.–W. Lee, 2005 .

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).

Predicting Glaucomatous Progression from Baseline HRT and SAP Measurements Using Relevance Vector Machine (RVM) Classifiers

Christopher Bowd, Linda M. Zangwill, Felipe A. Medeiros, 2011 .

Recognizing patterns of visual field loss using unsupervised machine learning

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

Unsupervised Machine Learning With Independent Component Analysis Identifies Patterns of Glaucomatous Visual Field Loss in SITA Fields

Christopher Bowd, Jiucang Hao, Linda M. Zangwill, 2009 .

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

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

Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns pdf

Michael H. Goldbaum, Nima Hatami, 2016, ArXiv.

Probability of Glaucoma Determined from Standard Automated Perimetry and from Optic Disk Topography using Relevance Vector Machine Classifiers

Terrence J. Sejnowski, Christopher Bowd, Jiucang Hao, 2004 .

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.

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..

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.

Image Understanding for Automated Retinal Diagnosis.

Michael H. Goldbaum, Mark E. Nelson, Subhasis Chaudhuri, 1989 .

Analysis of Glaucomatous Visual Field Patterns Found with Unsupervised Learning Using Indpendent Component Analysis and Principal Component Analysis

Terrence J. Sejnowski, T.–W. Lee, Pamela A. Sample, 2002 .

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.

Unsupervised machine learning to recognize glaucoma defect patterns and detect progression in RNFL thickness measurements

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

Shortened Perimetry for Glaucoma With Top 10 Locations Derived by Feature Selection With Machine Learning Classifiers

Terrence J. Sejnowski, Pamela A. Sample, Robert N. Weinreb, 2003 .

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

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

Glaucomatous patterns of binocular visual field loss identified by unsupervised machine learning

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

Diabetic retinopathy detection from image to classification using deep convolutional neural network

Christopher Bowd, Linda M. Zangwill, Michael H. Goldbaum, 2016 .

Simple Method for Curving Commercially Available Vitrectomy Cutter and Instruments During Surgery

Michael H. Goldbaum, S. Barone, Henry Ferreyra, 2010 .

Individualized deep learning auto-encoder (DL-AE)-based OCT deviation maps for improved glaucoma progression detection

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

Image Acquisition And Storage For Ophthalmic Fluorescein Angiography

Michael H. Goldbaum, Paul G. Rehkopf, Mark R. Nelson, 1984, Other Conferences.

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

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

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

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

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.

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 Development Of Abnormal Standard Visual Fields In Ocular Hypertensive Eyes: Machine Learning Classifiers And Statpac-like Analysis

Terrence J. Sejnowski, T.–W. Lee, Pamela A. Sample, 2002 .

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.

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.

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.