用扩散模型生成逼真子宫MRI，解决数据少隐私难问题
Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models
- 结合无条件与条件扩散模型，2D/3D联合生成子宫MRI
- 合成图像与真实扫描相似度高，诊断任务准确率显著提升
- 公开隐私保护模型与数据集，助力妇科AI公平发展
尽管生成模型进展显著，现有扩散模型在生成解剖结构精确的女性盆腔图像方面仍存挑战，限制了其在妇科影像中的应用，尤其受制于数据稀缺与患者隐私。为此，我们提出一种新型基于扩散的子宫MRI合成框架，融合2D与3D的无条件及条件去噪扩散概率模型（DDPM）与潜在扩散模型（LDM）。该方法生成解剖一致、高保真的合成图像，高度模拟真实扫描，可为训练鲁棒诊断模型提供宝贵资源。通过先进感知与分布度量评估生成质量，对比标准重建方法，我们在关键分类任务中实现显著诊断准确率提升。盲法专家评估进一步验证了合成图像的临床真实性。我们发布了具备隐私保护机制的模型与完整合成子宫MRI数据集，支持可复现研究，推动妇科AI的公平发展。
原文摘要 · Abstract (English)
Despite significant progress in generative modelling, existing diffusion models often struggle to produce anatomically precise female pelvic images, limiting their application in gynaecological imaging, where data scarcity and patient privacy concerns are critical. To overcome these barriers, we introduce a novel diffusion-based framework for uterine MRI synthesis, integrating both unconditional and conditioned Denoising Diffusion Probabilistic Models (DDPMs) and Latent Diffusion Models (LDMs) in 2D and 3D. Our approach generates anatomically coherent, high fidelity synthetic images that closely mimic real scans and provide valuable resources for training robust diagnostic models. We evaluate generative quality using advanced perceptual and distributional metrics, benchmarking against standard reconstruction methods, and demonstrate substantial gains in diagnostic accuracy on a key classification task. A blinded expert evaluation further validates the clinical realism of our synthetic images. We release our models with privacy safeguards and a comprehensive synthetic uterine MRI dataset to support reproducible research and advance equitable AI in gynaecology.
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