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ARVO Annual Meeting Abstract June 2022

Evaluation of a Deep Learning Model on a Real-World Clinical Glaucoma Dataset

Kaveri Thakoor; Ari Leshno; Sol La Bruna; Emmanouil Tsamis; Gustavo De Moraes; Paul Sajda; Noga Harizman; Jeffrey M Liebmann; George A Cioffi; Donald C Hood

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2026 – A0467. doi:

ARVO Annual Meeting Abstract June 2021

A Hybrid Deep Learning System to Distinguish Late Stages of AMD and to Compare Expert vs. Machine AMD Risk Features

Kaveri Thakoor; Darius Bordbar; Jiaang Yao; Omar Moussa; Weijie Lin; Ioana Scherbakova; Vlad Diaconita; Paul Sajda; Royce Chen

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):2146. doi:

Articles April 2021

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans

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Kaveri A. Thakoor; Xinhui Li; Emmanouil Tsamis; Zane Z. Zemborain; Carlos Gustavo De Moraes; Paul Sajda; Donald C. Hood
TAGS: convolutional neural networks, datasets, glaucoma, reference standards, retinal nerve fiber layer, training, maps
Trans. Vis. Sci. Tech.. 2021; 10(4):16. doi: https://doi.org/10.1167/tvst.10.4.16

Includes: Supplemental Content

ARVO Annual Meeting Abstract June 2020

Impact of Reference Standard, Data Augmentation, and OCT Input on Glaucoma Detection Accuracy by CNNs on a New Test Set

Kaveri Thakoor; Emmanouil (Manos) Tsamis; C Gustavo De Moraes; Paul Sajda; Donald C Hood

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4540. doi:

ARVO Annual Meeting Abstract July 2019

Assessing the Ability of Convolutional Neural Networks to Detect Glaucoma from OCT Probability Maps

Kaveri A Thakoor; Qian Zheng; Linyong Nan; Xinhui Li; Emmanouil (Manos) Tsamis; Rashmi Rajshekhar; Isht Dwivedi; Iddo Drori; Paul Sajda; Donald C Hood

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):1464. doi:

ARVO Annual Meeting Abstract June 2015

Hyperspectral Autofluorescence (AF) Characterization of Drusen in Donor Eyes with Age-Related Macular Degeneration (AMD)

Yuehong Tong; Tal Ben Ami; Ansh Johri; Robert Post; Zsolt Ablonczy; Paul Sajda; Christine A Curcio; Thomas Ach; Theodore Smith

Invest. Ophthalmol. Vis. Sci.. 2015; 56(7 ):3956. doi:

ARVO Annual Meeting Abstract June 2015

Ex Vivo Hyperspectral Autofluorescence (AF) Imaging of Retinal Pigment Epithelium (RPE) in Human Donor Eyes with Age-related Macular Degeneration (AMD)

Tal Ben Ami; Yuehong Tong; Paul Sajda; Zsolt Ablonczy; Christine A Curcio; Theodore Smith; Thomas Ach

Invest. Ophthalmol. Vis. Sci.. 2015; 56(7 ):4369. doi:

ARVO Annual Meeting Abstract April 2014

Simultaneous decomposition of multiple retinal pigment epithelium (RPE) autofluorescence hyperspectral datasets for fluorophor discovery

Robert Post; Ansh Johri; Bhaskar Ganti; Paul Sajda; Thomas Ach; Christine A Curcio; Theodore Smith

Invest. Ophthalmol. Vis. Sci.. 2014; 55(13):263. doi:

ARVO Annual Meeting Abstract April 2014

Mathematical modeling of retinal pigment epithelium (RPE) autofluorescence (AF) with Gaussian mixture models and non-negative matrix factorization (NMF)

Ansh Johri; Robert Post; Bhaskar Ganti; Amani A Fawzi; Paul Sajda; Thomas Ach; Christine A Curcio; Theodore Smith

Invest. Ophthalmol. Vis. Sci.. 2014; 55(13):3827. doi:

ARVO Annual Meeting Abstract April 2014

Quantitation of Hyperspectral Autofluorescence (AF) from Human Retinal Pigment Epithelium (RPE) Ex Vivo

Camellia Nabati; Ansh Johri; Robert Post; Paul Sajda; Thomas Ach; Christine A Curcio; Theodore Smith

Invest. Ophthalmol. Vis. Sci.. 2014; 55(13):3844. doi:

ARVO Annual Meeting Abstract April 2014

Hyperspectral image analysis of ex vivo autofluorescence (AF) of human Bruch’s membrane (BrM)

Aalya Fatoo; Ansh Johri; Rob Post; Paul Sajda; Christine A Curcio; Thomas Ach; Theodore Smith

Invest. Ophthalmol. Vis. Sci.. 2014; 55(13):3845. doi:

ARVO Annual Meeting Abstract April 2014

Hyperspectral Imaging Evidence for Four Abundant Retinal Pigment Epithelium (RPE) Fluorophors Across Age and Retinal Location

Kanika Agarwal; Robert Post; Ansh Johri; Alicia Cowley; Paul Sajda; Thomas Ach; Christine A Curcio; Theodore Smith

Invest. Ophthalmol. Vis. Sci.. 2014; 55(13):5242. doi:

Article November 2013

EEG precursors of detected and missed targets during free-viewing search

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João C. Dias; Paul Sajda; Jacek P. Dmochowski; Lucas C. Parra
TAGS: electroencephalography, saccades
Journal of Vision. 2013; 13(13):13. doi: https://doi.org/10.1167/13.13.13

Article December 2011

Decoding simulated neurodynamics predicts the perceptual consequences of age-related macular degeneration

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Jianing V. Shi; Jim Wielaard; R. Theodore Smith; Paul Sajda
TAGS: age-related macular degeneration, drusen, neurons, primary visual cortex, psychometrics, psychophysics, scotoma, blindness, impairment
Journal of Vision. 2011; 11(14):4. doi: https://doi.org/10.1167/11.14.4

ARVO Annual Meeting Abstract April 2010

Coupling Retinal Imaging With Psychophysics to Assess Perceptual Consequences of AMD

J. Shi; J. Wielaard; R. Smith; P. Sajda

Invest. Ophthalmol. Vis. Sci.. 2010; 51(13):1044. doi:

ARVO Annual Meeting Abstract April 2010

On Hyperspectral Signatures of Drusen

N. lee; A. A. Fawzi; P. Sajda; A. F. Laine; G. Martin; M. S. Humayun; R. T. Smith

Invest. Ophthalmol. Vis. Sci.. 2010; 51(13):4934. doi:

Vision Sciences Society Annual Meeting Abstract August 2009

We find before we Look: Neural signatures of target detection preceding saccades during visual search

An Luo; Lucas Parra; Paul Sajda

Journal of Vision. 2009; 9(8):1207. doi: https://doi.org/10.1167/9.8.1207

ARVO Annual Meeting Abstract April 2009

Perceptual Consequences of Macular Disease Evaluated Using a Model of V1

J. Shi; J. Wielaard; M. Busuioc; R. T. Smith; P. Sajda

Invest. Ophthalmol. Vis. Sci.. 2009; 50(13):3057. doi:

ARVO Annual Meeting Abstract May 2008

Using a Spiking Neuron Model of V1 as a Substrate for Mapping Visual Stimuli to Perception

J. Shi; J. Wielaard; M. Busuioc; R. T. Smith; P. Sajda

Invest. Ophthalmol. Vis. Sci.. 2008; 49(13):4497. doi:

ARVO Annual Meeting Abstract May 2007

Accessing the Cortical Response to Macular Disease via a Large-Scale Spiking Neuron Model of V1

P. Sajda; J. Wielaard; J. Shi; R. T. Smith

Invest. Ophthalmol. Vis. Sci.. 2007; 48(13):2347. doi:

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Evaluation of a Deep Learning Model on a Real-World Clinical Glaucoma Dataset

A Hybrid Deep Learning System to Distinguish Late Stages of AMD and to Compare Expert vs. Machine AMD Risk Features

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans

Impact of Reference Standard, Data Augmentation, and OCT Input on Glaucoma Detection Accuracy by CNNs on a New Test Set

Assessing the Ability of Convolutional Neural Networks to Detect Glaucoma from OCT Probability Maps

Hyperspectral Autofluorescence (AF) Characterization of Drusen in Donor Eyes with Age-Related Macular Degeneration (AMD)

Ex Vivo Hyperspectral Autofluorescence (AF) Imaging of Retinal Pigment Epithelium (RPE) in Human Donor Eyes with Age-related Macular Degeneration (AMD)

Simultaneous decomposition of multiple retinal pigment epithelium (RPE) autofluorescence hyperspectral datasets for fluorophor discovery

Mathematical modeling of retinal pigment epithelium (RPE) autofluorescence (AF) with Gaussian mixture models and non-negative matrix factorization (NMF)

Quantitation of Hyperspectral Autofluorescence (AF) from Human Retinal Pigment Epithelium (RPE) Ex Vivo

Hyperspectral image analysis of ex vivo autofluorescence (AF) of human Bruch’s membrane (BrM)

Hyperspectral Imaging Evidence for Four Abundant Retinal Pigment Epithelium (RPE) Fluorophors Across Age and Retinal Location

EEG precursors of detected and missed targets during free-viewing search

Decoding simulated neurodynamics predicts the perceptual consequences of age-related macular degeneration

Coupling Retinal Imaging With Psychophysics to Assess Perceptual Consequences of AMD

On Hyperspectral Signatures of Drusen

We find before we Look: Neural signatures of target detection preceding saccades during visual search

Perceptual Consequences of Macular Disease Evaluated Using a Model of V1

Using a Spiking Neuron Model of V1 as a Substrate for Mapping Visual Stimuli to Perception

Accessing the Cortical Response to Macular Disease via a Large-Scale Spiking Neuron Model of V1

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