Deep learning-based automated diagnosis of lung cancer has emerged as a crucial advancement that enables healthcare professionals to detect and initiate treatment earlier. However, these models require extensive training datasets with diverse case-specific properties. High-quality annotated data is particularly challenging to obtain, especially for cases with subtle pulmonary nodules that are difficult to detect even for experienced radiologists. This scarcity of well-labeled datasets can limit model performance and generalization across different patient populations. Digitally reconstructed radiographs (DRR) using CT-Scan to generate synthetic frontal chest X-rays with artificially inserted lung nodules offers one potential solution. However, this approach suffers from significant image quality degradation, particularly in the form of blurred anatomical features and loss of fine lung field structures. To overcome this, we introduce DiffusionXRay, a novel image restoration pipeline for Chest X-ray images that synergistically leverages denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs). DiffusionXRay incorporates a unique two-stage training process: First, we investigate two independent approaches, DDPM-LQ and GAN-based MUNIT-LQ, to generate low-quality CXRs, addressing the challenge of training data scarcity, posing this as a style transfer problem. Subsequently, we train a DDPM-based model on paired low-quality and high-quality images, enabling it to learn the nuances of X-ray image restoration. Our method demonstrates promising results in enhancing image clarity, contrast, and overall diagnostic value of chest X-rays while preserving subtle yet clinically significant artifacts, validated by both quantitative metrics and expert radiological assessment.

翻译：基于深度学习的肺癌自动诊断已成为一项关键进展，使医疗专业人员能够更早地发现并开始治疗。然而，这些模型需要具有多样化病例特异性特征的大规模训练数据集。高质量标注数据的获取尤其困难，特别是对于那些包含细微肺结节的病例，这些结节即使对于经验丰富的放射科医师也难以检测。这种高质量标注数据集的稀缺性可能限制模型在不同患者群体中的性能和泛化能力。利用CT扫描生成数字化重建放射影像（DRR），并人工插入肺结节以合成胸部正位X光片，提供了一种潜在的解决方案。然而，该方法存在显著的图像质量下降问题，主要表现为解剖特征模糊和肺部精细结构丢失。为克服这一局限，我们提出了DiffusionXRay，一种用于胸部X光图像的新型图像恢复流程，它协同利用了去噪扩散概率模型（DDPMs）和生成对抗网络（GANs）。DiffusionXRay采用独特的两阶段训练过程：首先，我们研究了两种独立方法——DDPM-LQ和基于GAN的MUNIT-LQ——来生成低质量胸部X光片，将训练数据稀缺问题转化为风格迁移任务进行处理。随后，我们在配对的低质量与高质量图像上训练一个基于DDPM的模型，使其能够学习X光图像恢复的细微特征。我们的方法在提升胸部X光图像清晰度、对比度及整体诊断价值方面展现出良好前景，同时保留了细微但具有临床意义的影像特征，该效果已通过定量指标和放射学专家评估得到验证。