Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence

Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence

Purpose: Developing robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-chosen data that can prove challenging to collect because of privacy concerns, disease rarity, or diagnostic label quality. Collecting image-based datasets for retinop...

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Bibliographic Details
Main Authors: Aaron S. Coyner, PhD, Jimmy S. Chen, MD, Ken Chang, PhD, Praveer Singh, PhD, Susan Ostmo, MS, R. V. Paul Chan, MD, MSc, Michael F. Chiang, MD, MA, Jayashree Kalpathy-Cramer, PhD, J. Peter Campbell, MD, MPH
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Ophthalmology Science
Subjects:
Artificial intelligence
Deep learning
Generative adversarial network
Retinopathy of prematurity
Online Access:http://www.sciencedirect.com/science/article/pii/S266691452200015X

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http://www.sciencedirect.com/science/article/pii/S266691452200015X

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