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AUBADE-syn: a novel deep learning ensemble method for glaucoma detection using synthetic fundus images on imbalanced
Fengze Wu1,2, Yuan Xue2, Phillip T Yuhas3
1Department of Ophthalmology and Visual Sciences, College of Medicine, The Ohio State University, Columbus, OH, USA.
NPJ Digital Medicine
|May 14, 2026
Summary
AUBADE-syn, a novel deep learning framework, enhances glaucoma detection by generating synthetic images to balance imbalanced medical datasets. This approach significantly improves early detection accuracy for irreversible blindness.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Glaucoma is a primary cause of irreversible blindness globally.
- Early detection is critical for vision preservation.
- Class imbalance in medical datasets hinders deep learning model performance.
Purpose of the Study:
- To introduce AUBADE-syn, a deep learning ensemble framework.
- To address class imbalance in glaucoma detection datasets.
- To improve model generalization and performance on imbalanced medical imaging tasks.
Main Methods:
- Developed AUBADE-syn, integrating synthetic image generation with class-balancing strategies.
- Utilized optic nerve head-centered regions and a classifier-free guided diffusion model.
- Generated realistic glaucomatous images to augment the minority class.
Main Results:
- AUBADE-syn achieved an AUC of 0.992 on the imbalanced EyePACS dataset, outperforming existing methods.
- Validated superior performance across ten independent public datasets.
- Demonstrated consistent improvement in discrimination and calibration for glaucoma detection.
Conclusions:
- AUBADE-syn effectively addresses class imbalance in glaucoma detection.
- Domain-aware synthetic augmentation and ensemble learning enhance imbalanced medical imaging tasks.
- The framework shows significant promise for early and accurate glaucoma diagnosis.
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