Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization

Author(s)

Oliveira, Guilherme C. ; Rosa, Gustavo H. ; Pedronette, Daniel C. G. ; Papa, Joao P. ; Kumar, Himeesh ; Passos, Leandro A. ; Kumar, Dinesh

• Document type: Journal article
• Source: Biomedical Signal Processing and Control; v. 94, p. 9-pg., 2024-04-03.

Abstract

Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literature, but neither a robust comparison of the different methods nor generalizability has been reported. Our approach integrates a retinal image quality assessment model and StyleGAN2 architecture to enhance Age-related Macular Degeneration (AMD) detection capabilities and improve generalizability. This work compares ten different Generative Adversarial Network (GAN) architectures to generate synthetic eye-fundus images with and without AMD. We combined subsets of three public databases (iChallenge-AMD, ODIR-2019, and RIADD) to form a single training and test set. We employed the STARE dataset for external validation, ensuring a comprehensive assessment of the proposed approach. The results show that StyleGAN2 reached the lowest Fr & eacute;chet Inception Distance (166.17), and clinicians could not accurately differentiate between real and synthetic images. ResNet-18 architecture obtained the best performance with 85% accuracy and outperformed the two human experts (80% and 75%) in detecting AMD fundus images. The accuracy rates were 82.8% for the test set and 81.3% for the STARE dataset, demonstrating the model's generalizability. The proposed methodology for synthetic medical image generation has been validated for robustness and accuracy, with free access to its code for further research and development in this field. (AU)

• FAPESP's process: 19/02205-5 - Adversarial learning in natural language processing
• Grantee: Gustavo Henrique de Rosa
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 19/00585-5 - Evolutionary Generative Adversarial Networks applied to computer-assisted diabetic retinopathy diagnosis
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 23/10823-6 - On the Study and Development of Biological Plausible Computational Intelligent Models
• Grantee: Leandro Aparecido Passos Junior
• Support Opportunities: Scholarships in Brazil - Support Program for Fixating Young Doctors
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
• Grantee: Daniel Carlos Guimarães Pedronette
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC