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WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models
Héctor Anaya-Sánchez1, Leopoldo Altamirano-Robles1, Raquel Díaz-Hernández2
1Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Sensors (Basel, Switzerland)
|January 11, 2025
Summary
This study introduces a novel method for generating synthetic medical images to combat data scarcity in diabetic retinopathy classification. The approach significantly improves the quality and realism of generated retinal images, aiding diagnostic accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Data scarcity is a major challenge in medical image classification.
- Accurate synthetic image generation is vital for improving diagnostic models.
- Diabetic retinopathy classification requires high-quality, diverse datasets.
Purpose of the Study:
- To develop a novel method for generating high-quality synthetic medical images for diabetic retinopathy classification.
- To enhance training datasets using realistic retinal images with preserved pathological features.
- To address data scarcity in sensor-derived medical imaging.
Main Methods:
- Utilized a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP).
- Incorporated nearest-neighbor interpolation for image generation.
- Evaluated performance on multiple retinal image datasets (Retinal-Lesions, FGADR, IDRiD, Kaggle).
Main Results:
- Achieved superior performance compared to traditional generative models (e.g., conditional GANs, PathoGAN).
- Obtained excellent metrics on the Kaggle dataset: FID of 15.21, MSE of 0.002025, SSIM of 0.89.
- Expert evaluation showed only 56.66% of synthetic images were distinguishable from real ones, indicating high fidelity.
Conclusions:
- The proposed WGAN-GP based method effectively generates realistic synthetic retinal images.
- This approach enhances medical image classification by providing high-fidelity, diverse training data.
- The method shows significant potential for improving diabetic retinopathy diagnosis and other medical imaging tasks.
