PRGAN: A Progressive Refined GAN for Lesion Localization and Segmentation on High-Resolution Retinal Fundus Photography

Chen, Ao; Ma, Xiao; Chen, Qiang; Ji, Zexuan

doi:10.1007/978-3-031-18910-4_23

Cited by 1 publication

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GANs possess the unique capability to generate synthetic images that mimic normal fundus photos. By comparing these generated normal images with diseased ones, GAN-based models can effectively identify and differentiate lesions [ 13 ].…”

Section: Main Textmentioning

confidence: 99%

“…Most of the recent segmentation models are based on CNN which can improve generalization, automatically extract features, have higher robustness to variation of image quality, and are more efficient and capable of multitasking compared to traditional ML. In recent years, generative adversarial networks (GANs) have also been used in segmentation tasks for retinal lesions [13,14]. GANs possess the unique capability to generate synthetic images that mimic normal fundus photos.…”

Section: Ai Models For Dr Lesion Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema

Yao,

Lim,

Lim

et al. 2024

Eye and Vis

View full text Add to dashboard Cite

Background Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. Main text This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. Conclusion AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

show abstract

Section: Main Textmentioning

confidence: 99%

Section: Ai Models For Dr Lesion Segmentationmentioning

confidence: 99%