World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
5117
World Ranking
13065
National Ranking
5261

Overview

Philippe Burlina is affiliated with Johns Hopkins University in the United States. Their research primarily spans the fields of computer science and medicine, with a significant focus on artificial intelligence and medical imaging.

Their publications cover various topics related to artificial intelligence applications in medical diagnostics, particularly in ophthalmology and retinal imaging. Notable recent papers include:

  • Addressing Artificial Intelligence Bias in Retinal Diagnostics (2021), published in Translational Vision Science & Technology
  • Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases (2020), published in JAMA Ophthalmology
  • AI-based detection of erythema migrans and disambiguation against other skin lesions (2020), published in Computers in Biology and Medicine
  • Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning (2021), published in JAMA Ophthalmology
  • Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema (2023), published in JAMA Ophthalmology

Burlina frequently collaborates with several co-authors, including William Paul, Neil Joshi, Haolin Yuan, and Yinzhi Cao. William Paul appears as a co-author in multiple publications, indicating an ongoing research partnership.

Their work has appeared in multiple publication venues, with a notable number of papers published in arXiv (Cornell University) and JAMA Ophthalmology. Other venues include the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Translational Vision Science & Technology, and Computers in Biology and Medicine.

The main fields of study for Burlina's research are:

  • Computer Science
  • Medicine

Their subfields of study reflect this interdisciplinary focus, covering:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Ophthalmology
  • Information Systems
  • Computer Vision and Pattern Recognition

The primary research topics addressed in their work include:

  • Adversarial Robustness in Machine Learning
  • Anomaly Detection Techniques and Applications
  • Retinal Imaging and Analysis
  • Privacy-Preserving Technologies in Data
  • Retinal Diseases and Treatments
  • Herpesvirus Infections and Treatments
  • Generative Adversarial Networks and Image Synthesis

Best Publications

  • Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

    Philippe M. Burlina;Neil Joshi;Michael Pekala;Katia D. Pacheco

  • A support vector method for anomaly detection in hyperspectral imagery

    A. Banerjee;P. Burlina;C. Diehl

  • Comparing humans and deep learning performance for grading AMD

    Philippe Burlina;Katia D. Pacheco;Neil Joshi;David E. Freund

  • AI for medical imaging goes deep.

    Daniel S W Ting;Daniel S W Ting;Yong Liu;Philippe Burlina;Xinxing Xu

  • Validating retinal fundus image analysis algorithms: issues and a proposal.

    Emanuele Trucco;Alfredo Ruggeri;Thomas Karnowski;Luca Giancardo

  • Deep learning based retinal OCT segmentation

    M. Pekala;N. Joshi;T.Y. Alvin Liu;N.M. Bressler

  • Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration

    Philippe M. Burlina;Philippe M. Burlina;Neil Joshi;Katia D. Pacheco;T. Y. Alvin Liu

  • Detection of age-related macular degeneration via deep learning

    P. Burlina;D. E. Freund;N. Joshi;Y. Wolfson

  • Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

    Philippe Burlina;Seth Billings;Neil Joshi;Jemima Albayda

  • Addressing Artificial Intelligence Bias in Retinal Diagnostics

    Philippe Burlina;Philippe Burlina;Neil Joshi;William Paul;Katia D. Pacheco

  • Kernel fully constrained least squares abundance estimates

    J. Broadwater;R. Chellappa;A. Banerjee;P. Burlina

  • System and method of managing web content

    Albert Brown;Philippe Burlina;Stephane Depuy;Shuang Wang

  • Adaptive target detection in foliage-penetrating SAR images using alpha-stable models

    A. Banerjee;P. Burlina;R. Chellappa

  • Higher order statistical learning for vehicle detection in images

    A.N. Rajagopalan;P. Burlina;R. Chellappa

  • A system and method for automated detection of age related macular degeneration and other retinal abnormalities

    Neil Bressler;Philippe Martin Burlina;David Eric Freund

  • Practical Blind Membership Inference Attack via Differential Comparisons

    Bo Hui;Yuchen Yang;Haolin Yuan;Philippe Burlina

  • Fast Hyperspectral Anomaly Detection via SVDD

    A. Banerjee;P. Burlina;R. Meth

  • Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

    Albert K. Feeny;Mongkol Tadarati;David E. Freund;Neil M. Bressler

  • Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

    Philippe Burlina;Philippe Burlina;William Paul;Philip Mathew;Neil Joshi

  • Automated detection of drusen in the macula

    D.E. Freund;N. Bressler;P. Burlina

  • Image segmentation and labeling using the Polya urn model

    A. Banerjee;P. Burlina;F. Alajaji

  • Automated detection of erythema migrans and other confounding skin lesions via deep learning.

    Philippe M. Burlina;Philippe M. Burlina;Neil J. Joshi;Elise Ng;Seth D. Billings

  • Where's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection

    Philippe Burlina;Neil Joshi;I-Jeng Wang

Frequent Co-Authors

Rama Chellappa
Rama Chellappa Johns Hopkins University
Fady Alajaji
Fady Alajaji Queen's University
Daniel DeMenthon
Daniel DeMenthon Johns Hopkins University Applied Physics Laboratory
Larry S. Davis
Larry S. Davis University of Maryland, College Park
Neil Zhenqiang Gong
Neil Zhenqiang Gong Duke University
Gregory D. Hager
Gregory D. Hager Johns Hopkins University
Michael D. Abràmoff
Michael D. Abràmoff University of Iowa
Robert J. Greenberg
Robert J. Greenberg Johns Hopkins University
A.B. Cohen
A.B. Cohen Arizona State University
A. N. Rajagopalan
A. N. Rajagopalan Indian Institute of Technology Madras

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens doors to a variety of flexible and rewarding educational routes. For those seeking to quickly boost their credentials, there are several certifications for jobs that not only enhance your skills but are also recognized by employers for their value and practicality.

If your goal is to advance academically without spending years in school, consider enrolling in one of the quick masters degrees online. These programs are ideal for driven professionals seeking career growth in less time.

To ensure your investment pays off, focus on programs recognized as most worthwhile masters degrees. These specialized degrees are designed to meet the evolving demands of today’s tech job market.

For those just starting out, online associate degree programs are a practical way to enter the field, build foundational knowledge, and unlock entry-level positions. Whichever path you choose, online learning offers flexibility to juggle work, study, and personal life.

Best Scientists Citing Philippe Burlina

Trending Scientists

Recently Published Articles