Second Affiliated Hospital of Jilin University

The study pretrained StyleGAN3 and Medfusion on an auxiliary dataset, curated synthetic images through a domain-matched Oracle network using Latent Space Rejection Sampling and Class-Conditioned SDEdit Escalation, and evaluated downstream grading on the Indian DR Image Dataset across ConvNeXt, ResNet50, and VGG16 with five random seeds, finding that a real-data-only ConvNeXt baseline reached a quadratic weighted kappa of 0.6350 ± 0.0135, curated StyleGAN3 augmentation raised it to 0.7185 ± 0.0309, Medfusion augmentation reached 0.7695 ± 0.0254 (an absolute improvement of 0.1345), and Medfusion doubled proliferative DR recall from 0.2615 ± 0.1595 to 0.5385 ± 0.0942.

The study pretrained StyleGAN3 and Medfusion on an auxiliary dataset, curated synthetic images through a domain-matched Oracle network using Latent Space Rejection Sampling and Class-Conditioned SDEdit Escalation, and evaluated downstream grading on the Indian DR Image Dataset across ConvNeXt, ResNet50, and VGG16 with five random seeds, finding that a real-data-only ConvNeXt baseline reached a quadratic weighted kappa of 0.6350 ± 0.0135, curated StyleGAN3 augmentation raised it to 0.7185 ± 0.0309, Medfusion augmentation reached 0.7695 ± 0.0254 (an absolute improvement of 0.1345), and Medfusion doubled proliferative DR recall from 0.2615 ± 0.1595 to 0.5385 ± 0.0942.