Abstract
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.
Abstract (translated)
基于深度学习的肺癌自动诊断技术已经成为一项重要的进展，使医疗专业人员能够更早地检测和启动治疗。然而，这些模型需要大量的训练数据集，并且这些数据集中应包含各种特定病例的特点。特别是高质量的标注数据对于细微肺结节（即使是经验丰富的放射科医生也难以发现）的情况来说尤其难以获取。这种良好标记的数据缺乏可能会限制模型在不同患者群体中的表现和泛化能力。利用CT扫描生成合成胸部正位X光片，这种方法通过数字重建射线照片(DRR)并在图像中人工插入肺结节来提供了一种可能的解决方案。然而，这种方法会导致显著的图像质量下降，特别是在解剖特征模糊和细微肺野结构丢失方面。 为了解决这些问题，我们引入了DiffusionXRay，这是一种新的用于胸部X光片图像恢复的技术流程，它结合了去噪扩散概率模型（DDPM）和生成对抗网络（GAN）。DiffusionXRay采用了一种独特的两阶段训练过程：首先，我们研究两种独立的方法——DDPM-LQ 和 GAN-based MUNIT-LQ——来生成低质量的胸部X光片（CXRs），以此应对训练数据不足的问题，并将其视为一种风格迁移问题。然后，我们使用一对低质量和高质量图像对基于DDPM的模型进行训练，使其能够学习到X射线图像恢复中的细微差别。 我们的方法在提高胸部X光片的清晰度、对比度以及整体诊断价值方面显示出有希望的结果，并且验证了该方法能够在保留临床意义的微妙结构的同时保持良好的定性和专家放射学评估结果。
URL
https://arxiv.org/abs/2603.01686
