Privacy-preserving collaborative transfer learning with generative adversarial networks (CTL-GAN) for classification of dry eye disease

This study investigated whether a collaborative transfer learning generative adversarial network (CTL-GAN) can generate synthetic fluorescein corneal images and enhance multiclass dry eye disease (DED) grading across institutions. We retrospectively included 1,600 fluorescein-stained anterior segment images from patients with DED at Samsung Medical Center (SMC) and 260 images from the public Sun Yat-sen University (SYSU) dataset, graded on the Oxford scale. A StyleGAN3 generator trained on SMC data produced synthetic images. Pretrained generator weights were transferred to a simulated site represented by the SYSU dataset and fine-tuned on local images, without exchanging raw images. DenseNet classifiers trained with GAN-based augmentation were compared with a benchmark model trained on real images only. GAN-based augmentation improved validation accuracy on SMC data from 64.2% to 75.5%, and transferring pretrained weights increased SYSU validation accuracy from 41.4% to 66.3%. Peak signal-to-noise ratio (PSNR 13.95 ± 1.54 dB), structural similarity index (SSIM 0.428 ± 0.111), and learned perceptual image patch similarity (LPIPS 0.510 ± 0.047) showed low similarity to real images. In a blinded 10-item multiple-choice realism survey, 37 ophthalmologists answered correctly in 28.4% of cases, against a 20% chance level. CTL-GAN indicates that image synthesis from shared generator weights can improve external DED grading without exchanging patient images.