The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in synthetic data generation. While Generative Adversarial Networks (GANs) have shown promise across various applications, they still face challenges like mode collapse and insufficient coverage of real data distributions. This work explores the use of GAN ensembles to overcome these limitations, specifically in the context of medical imaging. By solving a multi-objective optimisation problem that balances fidelity and diversity, we propose a method for selecting an optimal ensemble of GANs tailored for medical data. The selected ensemble is capable of generating diverse synthetic medical images that are representative of true data distributions and computationally efficient. Each model in the ensemble brings a unique contribution, ensuring minimal redundancy. We conducted a comprehensive evaluation using three distinct medical datasets, testing 22 different GAN architectures with various loss functions and regularisation techniques. By sampling models at different training epochs, we crafted 110 unique configurations. The results highlight the capability of GAN ensembles to enhance the quality and utility of synthetic medical images, thereby improving the efficacy of downstream tasks such as diagnostic modelling.

翻译：生成式人工智能的进步，特别是在医学影像领域，面临着在合成数据生成中确保高保真度、多样性和高效性的三重挑战。尽管生成对抗网络（GANs）在各种应用中展现出潜力，但仍面临模式崩溃和对真实数据分布覆盖不足等挑战。本研究探索了使用GAN集成方法来克服这些限制，特别是在医学影像的背景下。通过求解一个平衡保真度与多样性的多目标优化问题，我们提出了一种为医疗数据定制选择最优GAN集成的方法。所选集成能够生成多样化的合成医学影像，这些影像既具有真实数据分布的代表性，又具备计算效率。集成中的每个模型都贡献独特价值，确保了冗余度最小化。我们使用三个不同的医学数据集进行了全面评估，测试了22种不同的GAN架构，涵盖多种损失函数和正则化技术。通过对不同训练周期下的模型进行采样，我们构建了110种独特配置。结果突显了GAN集成在提升合成医学影像质量与实用性方面的能力，从而增强了诊断建模等下游任务的有效性。