Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review DOI Creative Commons

Juliana Machado,

Ana Marta, Pedro Mestre

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(6), С. 3084 - 3084

Опубликована: Март 12, 2025

Inherited retinal diseases (IRDs) are rare and genetically diverse disorders that cause progressive vision loss affect 1 in 3000 individuals worldwide. Their rarity genetic variability pose a challenge for deep learning models due to the limited amount of data. Generative offer promising solution by creating synthetic data improve training datasets. This study carried out systematic literature review investigate use generative augment IRDs assess their impact on performance classifiers these diseases. Following PRISMA 2020 guidelines, searches four databases identified 32 relevant studies, 2 focused IRD rest other The results indicate effectively small Among techniques identified, Deep Convolutional Adversarial Networks (DCGAN) Style-Based Generator Architecture (StyleGAN2) were most widely used. These architectures generated highly realistic data, often indistinguishable from real even experts. highlight need more research into generation develop robust diagnostic tools studies comprehensive repositories.

Язык: Английский

Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review DOI Creative Commons

Juliana Machado,

Ana Marta, Pedro Mestre

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(6), С. 3084 - 3084

Опубликована: Март 12, 2025

Inherited retinal diseases (IRDs) are rare and genetically diverse disorders that cause progressive vision loss affect 1 in 3000 individuals worldwide. Their rarity genetic variability pose a challenge for deep learning models due to the limited amount of data. Generative offer promising solution by creating synthetic data improve training datasets. This study carried out systematic literature review investigate use generative augment IRDs assess their impact on performance classifiers these diseases. Following PRISMA 2020 guidelines, searches four databases identified 32 relevant studies, 2 focused IRD rest other The results indicate effectively small Among techniques identified, Deep Convolutional Adversarial Networks (DCGAN) Style-Based Generator Architecture (StyleGAN2) were most widely used. These architectures generated highly realistic data, often indistinguishable from real even experts. highlight need more research into generation develop robust diagnostic tools studies comprehensive repositories.

Язык: Английский

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