DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretraining
This addresses the problem of class imbalance in medical datasets for glaucoma detection, offering an incremental improvement over existing methods.
The paper tackled class imbalance in glaucoma detection by using diffusion models to generate synthetic data and pretraining, resulting in an improvement in harmonic mean from 89.09% to 92.59% on a national dataset.
Glaucoma is a progressive optic neuropathy characterized by structural damage to the optic nerve head and functional changes in the visual field. Detecting glaucoma early is crucial to preventing loss of eyesight. However, medical datasets often suffer from class imbalances, making detection more difficult for deep-learning algorithms. We use a generative-based framework to enhance glaucoma diagnosis, specifically addressing class imbalance through synthetic data generation. In addition, we collected the largest national dataset for glaucoma detection to support our study. The imbalance between normal and glaucomatous cases leads to performance degradation of classifier models. By combining our proposed framework leveraging diffusion models with a pretraining approach, we created a more robust classifier training process. This training process results in a better-performing classifier. The proposed approach shows promising results in improving the harmonic mean (sensitivity and specificity) and AUC for the roc for the glaucoma classifier. We report an improvement in the harmonic mean metric from 89.09% to 92.59% on the test set of our national dataset. We examine our method against other methods to overcome imbalance through extensive experiments. We report similar improvements on the AIROGS dataset. This study highlights that diffusion-based generation can be of great importance in tackling class imbalances in medical datasets to improve diagnostic performance.