MedWGAN Based Synthetic Dataset Generation for Uveitis Pathology - Réseaux, Informatique, Systèmes de Confiance Access content directly
Journal Articles Intelligent Systems with Applications Year : 2023

MedWGAN Based Synthetic Dataset Generation for Uveitis Pathology


Clinical decision support based on artificial intelligence (AI) methods have increasingly been employed in medical applications to support medical diagnosis. Developing efficient AI methods, however, depends necessarily on the availability of sufficiently large amount of data to provide reliable results. But, in medicine, it is not always possible to find sufficient amount of real data on all pathologies, particularly, for rare diseases. This paper proposes a methodological framework for generating synthetic data using data augmentation techniques combined with epidemiological profiles. It focuses on Uveitis, a rare disease in ophthalmology, which is difficult to diagnose because of the disparity in prevalence of its etiologies. The generated synthetic data have been qualitatively validated by specialist ophthalmologists and quantitatively tested using machine learning methods. Results show that, of a randomly selected sample of the generated data, more than 55% were assessed as good or excellent, which is very promising for generating synthetic, validated as near-real, medical data for rare diseases. They also show that the proposed framework is consistent in generating synthetic data, for Uveitis pathology, of different dataset sizes, achieving more than than 80% diagnosis prediction accuracy for 2000 patient records or larger.
Fichier principal
Vignette du fichier
MedWGAN-based-synthetic-dataset-generation-for-Uveitis-pathology.pdf (1.95 Mo) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

hal-04067238 , version 1 (26-04-2023)




Heithem Sliman, Imen Megdiche, Loay Ajramy, Adel Taweel, Sami Yangui, et al.. MedWGAN Based Synthetic Dataset Generation for Uveitis Pathology. Intelligent Systems with Applications, 2023, 18, pp.200223. ⟨10.1016/j.iswa.2023.200223⟩. ⟨hal-04067238⟩
175 View
66 Download



Gmail Mastodon Facebook X LinkedIn More