用不确定性引导生成暗场X光图像，提升医学影像诊断可靠性
Uncertainty-guided Generation of Dark-field Radiographs
- 基于不确定性的渐进式生成对抗网络，融合认知与随机不确定性
- 生成图像结构保真度高，量化指标在各阶段持续提升
- 适合医学影像增强与低数据场景下的生成模型研究者
X射线暗场摄影通过小角度散射揭示微结构组织变化，为传统衰减成像提供互补信息。然而，此类数据稀缺制约了深度学习模型的发展。本文提出首个直接从标准衰减胸部X光片生成暗场图像的框架，采用不确定性引导的渐进式生成对抗网络，同时建模熵性（aleatoric）与认知性（epistemic）不确定性，提升可解释性与可靠性。实验表明，生成图像具有高结构保真度，量化指标在各生成阶段持续优化。此外，分布外评估验证了模型良好的泛化能力。结果表明，不确定性引导的生成建模实现了真实感暗场图像合成，为未来临床应用奠定可靠基础。
原文摘要 · Abstract (English)
X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. However, the limited availability of such data poses challenges for developing robust deep learning models. In this work, we present the first framework for generating dark-field images directly from standard attenuation chest X-rays using an Uncertainty-Guided Progressive Generative Adversarial Network. The model incorporates both aleatoric and epistemic uncertainty to improve interpretability and reliability. Experiments demonstrate high structural fidelity of the generated images, with consistent improvement of quantitative metrics across stages. Furthermore, out-of-distribution evaluation confirms that the proposed model generalizes well. Our results indicate that uncertainty-guided generative modeling enables realistic dark-field image synthesis and provides a reliable foundation for future clinical applications.
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