Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy

This research leverages ConditionalStyleGAN to generate synthetic fundus images, which are then used to enhance the performance of a retinopathy grading classifier across various scenarios. The pretrained ConditionalStyleGAN model is located in the "CStyleGAN" folder. Our findings indicate that the best results for the grading classifier are achieved when it is initially pretrained with synthetic images and subsequently fine-tuned with real images. Detailed results, including all images used in

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