Michael Goldbaum | University of California, San Diego (original) (raw)

Papers by Michael Goldbaum

ArXiv, 2016

Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automa... more Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discove... more Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

We develop an Artificial Intelligence (AI) framework for glaucoma precognition from baseline conf... more We develop an Artificial Intelligence (AI) framework for glaucoma precognition from baseline confocal scanning laser ophthalmoscopy imaging data, using a convolutional neural network (CNN) model. The proposed framework extracts 'deep features' from convolutional layers of the CNN model, which are used as input to the ensemble learning classifier in order to identify patients that will likely convert to glaucoma after few years. The prediction model achieved area under the receiver operating characteristic curve (AUC) of 0.83 using the data from baseline visit. The model predicted the onset of glaucoma more accurately than known glaucoma risk factors, Glaucoma Probability Score (GPS) and Moorfields Regression Analysis (MRA) parameters of the Heidelberg Retinal Tomograph (HRT) software. The proposed AI construct provides a highly specific and sensitive model that can predict the onset of glaucoma from baseline HRT parameters and has the potential to provide clinicians valuable information regarding the onset of glaucoma.

Purpose: To assess the accuracy of deep learning models to predict glaucoma development from fund... more Purpose: To assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years prior to disease onset. Design: A deep learning model for prediction of glaucomatous optic neuropathy or visual field abnormality from color fundus photographs. Participants: We retrospectively included 66,721 fundus photographs from 3,272 eyes of 1,636 subjects to develop deep leaning models. Method: Fundus photographs and visual fields were carefully examined by two independent readers from the optic disc and visual field reading centers of the ocular hypertension treatment study (OHTS). When an abnormality was detected by the readers, subject was recalled for re-testing to confirm the abnormality and further confirmation by an endpoint committee. Using OHTS data, deep learning models were trained and tested using 85% of the fundus photographs and further validated (re-tested) on the remaining (held-out) 15% of the fundus photographs. Main Outcome Measures:...

Nature Medicine

Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical ca... more Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal. Medical information has become increasingly complex over time. The range of disease entities, diagnostic testing and biomark-ers, and treatment modalities has increased exponentially in recent years. Subsequently, clinical decision-making has also become more complex and demands the synthesis of decisions from assessment of large volumes of data representing clinical information. In the current digital age, the electronic health record (EHR) represents a massive repository of electronic data points representing a diverse array of clinical information 1-3. Artificial intelligence (AI) methods have emerged as potentially powerful tools to mine EHR data to aid in disease diagnosis and management, mimicking and perhaps even augmenting the clinical decision-making of human physicians 1. To formulate a diagnosis for any given patient, physicians frequently use hypotheticodeductive reasoning. Starting with the chief complaint, the physician then asks appropriately targeted questions relating to that complaint. From this initial small feature set, the physician forms a differential diagnosis and decides what features (historical questions, physical exam findings, laboratory testing, and/or imaging studies) to obtain next in order to rule in or rule out the diagnoses in the differential diagnosis set. The most useful features are identified, such that when the probability of one of the diagnoses reaches a predetermined level of acceptability, the process is stopped, and the diagnosis is accepted. It may be possible to achieve an acceptable level of certainty of the diagnosis with only a few features without having to process the entire feature set. Therefore, the physician can be considered a classifier of sorts. In this study, we designed an AI-based system using machine learning to extract clinically relevant features from EHR notes to mimic the clinical reasoning of human physicians. In medicine, machine learning methods have already demonstrated strong performance in image-based diagnoses, notably in radiology 2 , dermatology 4 , and ophthalmology 5-8 , but analysis of EHR data presents a number of difficult challenges. These challenges include the vast quantity of data, high dimensionality, data sparsity, and deviations

Investigative ophthalmology & visual science, 2018

To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT... more To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. The RNFL PCA...

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2018

Our purpose was to compare the characteristics of the retinal and choroidal lesions including cho... more Our purpose was to compare the characteristics of the retinal and choroidal lesions including choroidal nevus, choroidal melanoma and congenital hypertrophy of the retina pigment epithelium using conventional color fundus photography (CFP) and multicolor imaging (MCI). The paired images of patients with retinal or choroidal lesions were assessed for the visibility of lesion's border, halo and drusen using a grading scale (0-2). The area of the lesion was measured on both imaging modalities. The same grading was also done on the individual color channels of MCI for a further evaluation. Thirty-three eyes of 33 patients were included. There were no significant differences in the mean border, drusen and halo visibility scores between the two imaging modalities (p = 0.12, p = 0.70, p = 0.35). However, the mean area of the lesion was significantly smaller on MCI than that on CFP (14.9±3.3 versus 18.7±3.4 mm, p = 0.01). The appearance of choroidal and/ or retinal lesions on MCI may be...

Cell, Jan 22, 2018

The implementation of clinical-decision support algorithms for medical imaging faces challenges w... more The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facil...

Survey of ophthalmology, Jan 9, 2017

A 68-year-old woman with a recent history of blurring in the left eye had undergone mastectomy fo... more A 68-year-old woman with a recent history of blurring in the left eye had undergone mastectomy for breast cancer twenty years ago. A series of bone metastases started five years after her diagnosis. Examination of the optic nerve head of the left eye revealed an isolated epipapillary mass. Indocyanine green angiography displayed vessels within the mass, and fluorescein angiography demonstrated hyperfluorescence of the mass from vascular leakage plus lobular spots of blocked fluorescence. B-scan ultrasound revealed a hyperechoic elevated nodular mass on the optic disc. Spectral domain optical coherence tomography displayed a mass of spherules. Magnetic resonance imaging of the brain demonstrated metastatic tumors. She was diagnosed with an optic disc metastasis from her breast carcinoma.

Clinical & Experimental Ophthalmology, 2016

Tuberous sclerosis or tuberous sclerosis complex (TSC), one of the phakomatoses, is characterized... more Tuberous sclerosis or tuberous sclerosis complex (TSC), one of the phakomatoses, is characterized by hamartomas of the heart, kidney, brain, skin and eyes. Ophthalmologic examinations are required in all cases of TSC. Retinal hamartomas are the most common ocular finding in tuberous sclerosis. The majority of hamartomas are non-progressive; however, lesions with subretinal fluid and progression have been reported. This paper details the genetics, clinical features and ocular findings of TSC and reviews potential therapeutic options for ophthalmic manifestations.

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2015

Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affe... more Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affecting vessel caliber, occlusion, leakage, inflammation, and proliferation. We introduce a novel supervised method to evaluate performance of Leung-Malik filters in delineating vessels. First, feature vectors are extracted for every pixel with respect to the response of Leung-Malik filters on green channel retinal images in different orientations and scales. A two level hierarchical learning framework is proposed to segment vessels in retinal images with confounding disease abnormalities. In the first level, three expert classifiers are trained to delineate 1) vessels, 2) background, and 3) retinal pathologies including abnormal pathologies such as lesions and anatomical structures such as optic disc. In the second level, a new classifier is trained to detect vessels and non-vessel pixels based on results of the expert classifiers. Qualitative evaluation shows the effectiveness of the pro...

Archives of Ophthalmology, Aug 1, 1997

Retina J Retin Vitr Dis, 1984

Translational vision science & technology, 2016

To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian i... more To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of m...

International Conference on Image Processing, 1989

Optometry Vision Sci, 2008

Machine learning is concerned with the design and development of algorithms and techniques that a... more Machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn" patterns in data using iterative processes. Such processes can be supervised (guided by a priori group membership information) or unsupervised (guided by patterns within the data). Machine learning classifiers (MLC) are unconstrained by statistical assumptions and therefore are adaptable to complex data. Recent applications of MLC techniques to the detection and monitoring of glaucoma by analysis of visual field and optical imaging data suggest that these methods can provide improvement over currently available techniques. This article provides some background about the classification task in glaucoma and the structure and evaluation of MLCs, and it reviews MLC techniques as they have been applied to visual function and optical imaging in glaucoma research.

Archives of Ophthalmology, 1989

Retina J Retin Vitr Dis, 1990

Archives of Ophthalmology, Nov 1, 1991

... MD Associate Editors Walter J. Stark, MD Andrew P. Schachat, MD Administrative Assistant Anne... more ... MD Associate Editors Walter J. Stark, MD Andrew P. Schachat, MD Administrative Assistant Anne Meltzer, BA The Wilmer Institute The ... BOOK REVIEWS Antiviral Agents and Viral Diseases of Man (Galasso, Whitley, and Merigan) Reviewed by Michael B. Raizman, MD, Boston ...

Arvo Meeting Abstracts, May 1, 2003

ArXiv, 2016

Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automa... more Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discove... more Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

We develop an Artificial Intelligence (AI) framework for glaucoma precognition from baseline conf... more We develop an Artificial Intelligence (AI) framework for glaucoma precognition from baseline confocal scanning laser ophthalmoscopy imaging data, using a convolutional neural network (CNN) model. The proposed framework extracts 'deep features' from convolutional layers of the CNN model, which are used as input to the ensemble learning classifier in order to identify patients that will likely convert to glaucoma after few years. The prediction model achieved area under the receiver operating characteristic curve (AUC) of 0.83 using the data from baseline visit. The model predicted the onset of glaucoma more accurately than known glaucoma risk factors, Glaucoma Probability Score (GPS) and Moorfields Regression Analysis (MRA) parameters of the Heidelberg Retinal Tomograph (HRT) software. The proposed AI construct provides a highly specific and sensitive model that can predict the onset of glaucoma from baseline HRT parameters and has the potential to provide clinicians valuable information regarding the onset of glaucoma.

Purpose: To assess the accuracy of deep learning models to predict glaucoma development from fund... more Purpose: To assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years prior to disease onset. Design: A deep learning model for prediction of glaucomatous optic neuropathy or visual field abnormality from color fundus photographs. Participants: We retrospectively included 66,721 fundus photographs from 3,272 eyes of 1,636 subjects to develop deep leaning models. Method: Fundus photographs and visual fields were carefully examined by two independent readers from the optic disc and visual field reading centers of the ocular hypertension treatment study (OHTS). When an abnormality was detected by the readers, subject was recalled for re-testing to confirm the abnormality and further confirmation by an endpoint committee. Using OHTS data, deep learning models were trained and tested using 85% of the fundus photographs and further validated (re-tested) on the remaining (held-out) 15% of the fundus photographs. Main Outcome Measures:...

Nature Medicine

Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical ca... more Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal. Medical information has become increasingly complex over time. The range of disease entities, diagnostic testing and biomark-ers, and treatment modalities has increased exponentially in recent years. Subsequently, clinical decision-making has also become more complex and demands the synthesis of decisions from assessment of large volumes of data representing clinical information. In the current digital age, the electronic health record (EHR) represents a massive repository of electronic data points representing a diverse array of clinical information 1-3. Artificial intelligence (AI) methods have emerged as potentially powerful tools to mine EHR data to aid in disease diagnosis and management, mimicking and perhaps even augmenting the clinical decision-making of human physicians 1. To formulate a diagnosis for any given patient, physicians frequently use hypotheticodeductive reasoning. Starting with the chief complaint, the physician then asks appropriately targeted questions relating to that complaint. From this initial small feature set, the physician forms a differential diagnosis and decides what features (historical questions, physical exam findings, laboratory testing, and/or imaging studies) to obtain next in order to rule in or rule out the diagnoses in the differential diagnosis set. The most useful features are identified, such that when the probability of one of the diagnoses reaches a predetermined level of acceptability, the process is stopped, and the diagnosis is accepted. It may be possible to achieve an acceptable level of certainty of the diagnosis with only a few features without having to process the entire feature set. Therefore, the physician can be considered a classifier of sorts. In this study, we designed an AI-based system using machine learning to extract clinically relevant features from EHR notes to mimic the clinical reasoning of human physicians. In medicine, machine learning methods have already demonstrated strong performance in image-based diagnoses, notably in radiology 2 , dermatology 4 , and ophthalmology 5-8 , but analysis of EHR data presents a number of difficult challenges. These challenges include the vast quantity of data, high dimensionality, data sparsity, and deviations

Investigative ophthalmology & visual science, 2018

To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT... more To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. The RNFL PCA...

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2018

Our purpose was to compare the characteristics of the retinal and choroidal lesions including cho... more Our purpose was to compare the characteristics of the retinal and choroidal lesions including choroidal nevus, choroidal melanoma and congenital hypertrophy of the retina pigment epithelium using conventional color fundus photography (CFP) and multicolor imaging (MCI). The paired images of patients with retinal or choroidal lesions were assessed for the visibility of lesion's border, halo and drusen using a grading scale (0-2). The area of the lesion was measured on both imaging modalities. The same grading was also done on the individual color channels of MCI for a further evaluation. Thirty-three eyes of 33 patients were included. There were no significant differences in the mean border, drusen and halo visibility scores between the two imaging modalities (p = 0.12, p = 0.70, p = 0.35). However, the mean area of the lesion was significantly smaller on MCI than that on CFP (14.9±3.3 versus 18.7±3.4 mm, p = 0.01). The appearance of choroidal and/ or retinal lesions on MCI may be...

Cell, Jan 22, 2018

The implementation of clinical-decision support algorithms for medical imaging faces challenges w... more The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facil...

Survey of ophthalmology, Jan 9, 2017

A 68-year-old woman with a recent history of blurring in the left eye had undergone mastectomy fo... more A 68-year-old woman with a recent history of blurring in the left eye had undergone mastectomy for breast cancer twenty years ago. A series of bone metastases started five years after her diagnosis. Examination of the optic nerve head of the left eye revealed an isolated epipapillary mass. Indocyanine green angiography displayed vessels within the mass, and fluorescein angiography demonstrated hyperfluorescence of the mass from vascular leakage plus lobular spots of blocked fluorescence. B-scan ultrasound revealed a hyperechoic elevated nodular mass on the optic disc. Spectral domain optical coherence tomography displayed a mass of spherules. Magnetic resonance imaging of the brain demonstrated metastatic tumors. She was diagnosed with an optic disc metastasis from her breast carcinoma.

Clinical & Experimental Ophthalmology, 2016

Tuberous sclerosis or tuberous sclerosis complex (TSC), one of the phakomatoses, is characterized... more Tuberous sclerosis or tuberous sclerosis complex (TSC), one of the phakomatoses, is characterized by hamartomas of the heart, kidney, brain, skin and eyes. Ophthalmologic examinations are required in all cases of TSC. Retinal hamartomas are the most common ocular finding in tuberous sclerosis. The majority of hamartomas are non-progressive; however, lesions with subretinal fluid and progression have been reported. This paper details the genetics, clinical features and ocular findings of TSC and reviews potential therapeutic options for ophthalmic manifestations.

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2015

Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affe... more Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affecting vessel caliber, occlusion, leakage, inflammation, and proliferation. We introduce a novel supervised method to evaluate performance of Leung-Malik filters in delineating vessels. First, feature vectors are extracted for every pixel with respect to the response of Leung-Malik filters on green channel retinal images in different orientations and scales. A two level hierarchical learning framework is proposed to segment vessels in retinal images with confounding disease abnormalities. In the first level, three expert classifiers are trained to delineate 1) vessels, 2) background, and 3) retinal pathologies including abnormal pathologies such as lesions and anatomical structures such as optic disc. In the second level, a new classifier is trained to detect vessels and non-vessel pixels based on results of the expert classifiers. Qualitative evaluation shows the effectiveness of the pro...

Archives of Ophthalmology, Aug 1, 1997

Retina J Retin Vitr Dis, 1984

Translational vision science & technology, 2016

To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian i... more To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of m...

International Conference on Image Processing, 1989

Optometry Vision Sci, 2008

Machine learning is concerned with the design and development of algorithms and techniques that a... more Machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn" patterns in data using iterative processes. Such processes can be supervised (guided by a priori group membership information) or unsupervised (guided by patterns within the data). Machine learning classifiers (MLC) are unconstrained by statistical assumptions and therefore are adaptable to complex data. Recent applications of MLC techniques to the detection and monitoring of glaucoma by analysis of visual field and optical imaging data suggest that these methods can provide improvement over currently available techniques. This article provides some background about the classification task in glaucoma and the structure and evaluation of MLCs, and it reviews MLC techniques as they have been applied to visual function and optical imaging in glaucoma research.

Archives of Ophthalmology, 1989

Retina J Retin Vitr Dis, 1990

Archives of Ophthalmology, Nov 1, 1991

... MD Associate Editors Walter J. Stark, MD Andrew P. Schachat, MD Administrative Assistant Anne... more ... MD Associate Editors Walter J. Stark, MD Andrew P. Schachat, MD Administrative Assistant Anne Meltzer, BA The Wilmer Institute The ... BOOK REVIEWS Antiviral Agents and Viral Diseases of Man (Galasso, Whitley, and Merigan) Reviewed by Michael B. Raizman, MD, Boston ...

Arvo Meeting Abstracts, May 1, 2003