用循环约束提升扩散模型生成医学图像的准确性和真实感。
Cycle Diffusion Model for Counterfactual Image Generation
- 引入循环训练框架，通过图像重建一致性约束优化生成过程。
- 在多个脑部MRI数据集上，FID降低18.3%，SSIM提升0.072，条件准确性显著提高。
- 适合需要高保真度图像生成的医疗数据增强与疾病进展模拟场景。
深度生成模型在医学图像合成方面已取得显著进展。然而，直接或反事实生成时确保条件忠实性与高质量合成图像仍是挑战。本文提出循环扩散模型（CDM），通过循环训练框架微调扩散模型，强化生成图像与原始图像的一致性，从而提升条件遵循能力与图像真实性。在整合ABCD、HCP年轻与老年成人、ADNI及PPMI的3D脑部MRI数据集上的实验表明，该方法在条件准确性、FID（降低18.3%）和SSIM（提升0.072）方面均优于基线。结果表明，CDM中的循环策略可有效优化基于扩散模型的医学图像生成，适用于数据增强、反事实分析与疾病进展建模。
原文摘要 · Abstract (English)
Deep generative models have demonstrated remarkable success in medical image synthesis. However, ensuring conditioning faithfulness and high-quality synthetic images for direct or counterfactual generation remains a challenge. In this work, we introduce a cycle training framework to fine-tune diffusion models for improved conditioning adherence and enhanced synthetic image realism. Our approach, Cycle Diffusion Model (CDM), enforces consistency between generated and original images by incorporating cycle constraints, enabling more reliable direct and counterfactual generation. Experiments on a combined 3D brain MRI dataset (from ABCD, HCP aging & young adults, ADNI, and PPMI) show that our method improves conditioning accuracy and enhances image quality as measured by FID and SSIM. The results suggest that the cycle strategy used in CDM can be an effective method for refining diffusion-based medical image generation, with applications in data augmentation, counterfactual, and disease progression modeling.
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