Synthetic Medical Images for Robust, Privacy-Preserving Training of AI: Application to Retinopathy of Prematurity Diagnosis
Version 1Purpose: Developing robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-curated data, which can prove challenging to collect due to privacy concerns, disease rarity, or diagnostic label quality. Collecting datasets for training diagnostic models for retinopathy of prematurity (ROP), a potentially-blinding disease, suffers from all of these challenges. Progressively-growing generative adversarial networks (PGANs) may help, as they can s
No license available
Purpose: Developing robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-curated data, which can prove challenging to collect due to privacy concerns, disease rarity, or diagnostic label quality. Collecting datasets for training diagnostic models for retinopathy of prematurity (ROP), a potentially-blinding disease, suffers from all of these challenges. Progressively-growing generative adversarial networks (PGANs) may help, as they can s