Philippe Burlina
Philippe Burlina
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Cited by
Cited by
Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks
PM Burlina, N Joshi, M Pekala, KD Pacheco, DE Freund, NM Bressler
JAMA ophthalmology 135 (11), 1170-1176, 2017
A support vector method for anomaly detection in hyperspectral imagery
A Banerjee, P Burlina, C Diehl
IEEE Transactions on Geoscience and Remote Sensing 44 (8), 2282-2291, 2006
Deep learning in ophthalmology: the technical and clinical considerations
DSW Ting, L Peng, AV Varadarajan, PA Keane, PM Burlina, MF Chiang, ...
Progress in retinal and eye research 72, 100759, 2019
Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis
P Burlina, KD Pacheco, N Joshi, DE Freund, NM Bressler
Computers in biology and medicine 82, 80-86, 2017
Validating retinal fundus image analysis algorithms: issues and a proposal
E Trucco, A Ruggeri, T Karnowski, L Giancardo, E Chaum, ...
Investigative ophthalmology & visual science 54 (5), 3546-3559, 2013
AI for medical imaging goes deep
DSW Ting, Y Liu, P Burlina, X Xu, NM Bressler, TY Wong
Nature medicine 24 (5), 539-540, 2018
Deep learning based retinal OCT segmentation
M Pekala, N Joshi, TYA Liu, NM Bressler, DC DeBuc, P Burlina
Computers in biology and medicine 114, 103445, 2019
Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration
PM Burlina, N Joshi, KD Pacheco, DE Freund, J Kong, NM Bressler
JAMA ophthalmology 136 (12), 1359-1366, 2018
Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration
PM Burlina, N Joshi, KD Pacheco, TYA Liu, NM Bressler
JAMA ophthalmology 137 (3), 258-264, 2019
Detection of age-related macular degeneration via deep learning
P Burlina, DE Freund, N Joshi, Y Wolfson, NM Bressler
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 184-188, 2016
Kernel fully constrained least squares abundance estimates
J Broadwater, R Chellappa, A Banerjee, P Burlina
2007 IEEE international geoscience and remote sensing symposium, 4041-4044, 2007
Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods
P Burlina, S Billings, N Joshi, J Albayda
PloS one 12 (8), e0184059, 2017
Practical blind membership inference attack via differential comparisons
B Hui, Y Yang, H Yuan, P Burlina, NZ Gong, Y Cao
arXiv preprint arXiv:2101.01341, 2021
System and method of managing web content
A Brown, P Burlina, S Depuy, S Wang, D Anderson, J Wingen
US Patent App. 10/758,954, 2004
System and method for automated detection of age related macular degeneration and other retinal abnormalities
N Bressler, PM Burlina, DE Freund
US Patent 8,896,682, 2014
Adaptive target detection in foliage-penetrating SAR images using alpha-stable models
A Banerjee, P Burlina, R Chellappa
IEEE Transactions on Image Processing 8 (12), 1823-1831, 1999
Higher order statistical learning for vehicle detection in images
AN Rajagopalan, P Burlina, R Chellappa
Proceedings of the Seventh IEEE International Conference on Computer Vision …, 1999
Addressing artificial intelligence bias in retinal diagnostics
P Burlina, N Joshi, W Paul, KD Pacheco, NM Bressler
Translational Vision Science & Technology 10 (2), 13-13, 2021
Fast hyperspectral anomaly detection via SVDD
A Banerjee, P Burlina, R Meth
2007 IEEE International Conference on Image Processing 4, IV-101-IV-104, 2007
Patient-specific modeling and analysis of the mitral valve using 3D-TEE
P Burlina, C Sprouse, D DeMenthon, A Jorstad, R Juang, F Contijoch, ...
Information Processing in Computer-Assisted Interventions: First …, 2010
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