用生成模型集合提升医学图像合成的多样性与质量
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation
- 通过多目标优化选择最优生成对抗网络集合
- 在3个数据集上验证，生成图像更贴近真实分布且高效
- 适合需要高质量合成数据的医疗AI研发者
生成式人工智能在医学影像领域的进展面临保真度、多样性和效率三重挑战。尽管生成对抗网络（GAN）在多个应用中表现良好，但仍存在模式崩溃和真实数据分布覆盖不足等问题。本文探索使用GAN集合来克服这些限制，特别是在医学影像领域。通过解决平衡保真度与多样性的多目标优化问题，提出一种针对医学数据的最优GAN集合选择方法。所选集合能生成多样化、代表性强且计算高效的合成医学图像，集合内各模型贡献独特，冗余最小。我们使用三个不同医学数据集，测试了22种不同的GAN架构及多种损失函数和正则化技术，通过在不同训练阶段采样，构建了110种独特配置。结果表明，GAN集合显著提升了合成医学图像的质量与实用性，从而增强了下游任务（如诊断建模）的效果。
原文摘要 · Abstract (English)
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.
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