Beyond a single mode: GAN ensembles for diverse medical data generation

The advancement of generative AI in medical imaging faces the trilemma of simultaneously achieving high fidelity and diversity in synthetic data generation. Although Generative Adversarial Networks (GANs) have demonstrated significant potential, they are often hindered by limitations such as mode collapse and poor coverage of real data distributions. This study investigates the use of GAN ensembles as a solution to these challenges, with the goal of enhancing the quality and utility of synthetic medical images.

We formulate a multi-objective optimisation problem to select an optimal ensemble of GANs that balances fidelity and diversity. The ensemble comprises models that contribute uniquely to the synthetic data space, ensuring minimal redundancy. A comprehensive evaluation was conducted using three distinct medical imaging datasets. We tested 22 GAN architectures, incorporating various loss functions and regularisation techniques. By sampling models at different training epochs, we crafted 110 unique configurations for ensemble selection.

The selected GAN ensembles demonstrated improved performance in generating synthetic medical images that closely resemble real data distributions. These ensembles preserved image fidelity while increasing diversity. In some settings, downstream models trained on synthetic data achieved slightly higher accuracy than those trained on real data alone. This effect arises because the synthetic images act as a targeted data augmentation mechanism that enhances class balance and diversity rather than replacing real data.

GAN ensembles offer a robust solution to the fidelity-diversity-efficiency trade-off in medical image synthesis. By integrating multiple complementary models, the proposed approach improves the representativeness and utility of synthetic medical data, potentially advancing a wide range of clinical and research applications in diagnostic AI.