Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy
<|reference_start|>Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy: Diabetic retinopathy (DR) is a consequence of diabetes mellitus characterized by vascular damage within the retinal tissue. Timely detection is paramount to mitigate the risk of vision loss. However, training robust grading models is hindered by a shortage of annotated data, particularly for severe cases. This paper proposes a framework for controllably generating high-fidelity and diverse DR fundus images, thereby improving classifier performance in DR grading and detection. We achieve comprehensive control over DR severity and visual features (optic disc, vessel structure, lesion areas) within generated images solely through a conditional StyleGAN, eliminating the need for feature masks or auxiliary networks. Specifically, leveraging the SeFa algorithm to identify meaningful semantics within the latent space, we manipulate the DR images generated conditionally on grades, further enhancing the dataset diversity. Additionally, we propose a novel, effective SeFa-based data augmentation strategy, helping the classifier focus on discriminative regions while ignoring redundant features. Using this approach, a ResNet50 model trained for DR detection achieves 98.09% accuracy, 99.44% specificity, 99.45% precision, and an F1-score of 98.09%. Moreover, incorporating synthetic images generated by conditional StyleGAN into ResNet50 training for DR grading yields 83.33% accuracy, a quadratic kappa score of 87.64%, 95.67% specificity, and 72.24% precision. Extensive experiments conducted on the APTOS 2019 dataset demonstrate the exceptional realism of the generated images and the superior performance of our classifier compared to recent studies.<|reference_end|>
@article{pakdelmoez2024controllable,
    title={Controllable retinal image synthesis using conditional StyleGAN and
  latent space manipulation for improved diagnosis and grading of diabetic
  retinopathy},
    author={Somayeh Pakdelmoez (1), Saba Omidikia (1), Seyyed Ali Seyyedsalehi
  (1), Seyyede Zohreh Seyyedsalehi (2) ((1) Department of Biomedical
  Engineering, Amirkabir University of Technology, Tehran, Iran, (2) Department
  of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences,
  Islamic Azad University, Tehran, Iran)},
    journal={arXiv preprint arXiv:2409.07422},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07422},
    primaryClass={eess.IV cs.CV}
}