通过自适应掩码与跨域对齐，将有肺部阴影的X光片转为无阴影版本以提升诊断精度。
Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment
- 用自适应激活掩码精准修改肺部阴影区域。
- 在多个数据集上实现更低的FID和KID得分，翻译质量更优。
- 显著提升肺部边界分割与病灶分类准确率，适合临床辅助诊断场景。
胸部X光片（CXRs）在心肺疾病诊断与监测中具有关键作用。然而，肺部阴影常遮挡解剖结构，阻碍肺边界的清晰识别，影响病灶定位，严重降低分割精度与病变判读能力。为此，本研究提出一种无配对的CXR图像翻译框架，可将含肺部阴影的图像转换为无阴影版本，同时保留语义特征。核心方法是采用自适应激活掩码，选择性地修改肺部阴影区域；通过跨域对齐机制，使生成图像与预训练的CXR病灶分类器的特征图及预测标签保持一致，增强翻译过程的可解释性。我们在RSNA、MIMIC-CXR-JPG和JSRT数据集上验证该方法，结果显示其翻译质量优于现有方法（FID: 67.18 vs. 210.4，KID: 0.01604 vs. 0.225）。在RSNA肺部阴影、MIMIC ARDS患者及JSRT数据集上的评估表明，该方法显著提升了肺边界分割准确率（如RSNA：mIoU 76.58% vs. 62.58%，敏感度85.58% vs. 77.03%）与病灶分类性能，凸显其在临床中的应用潜力。
原文摘要 · Abstract (English)
Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opacities in CXRs frequently obscure anatomical structures, impeding clear identification of lung borders and complicating the localization of pathology. This challenge significantly hampers segmentation accuracy and precise lesion identification, which are crucial for diagnosis. To tackle these issues, our study proposes an unpaired CXR translation framework that converts CXRs with lung opacities into counterparts without lung opacities while preserving semantic features. Central to our approach is the use of adaptive activation masks to selectively modify opacity regions in lung CXRs. Cross-domain alignment ensures translated CXRs without opacity issues align with feature maps and prediction labels from a pre-trained CXR lesion classifier, facilitating the interpretability of the translation process. We validate our method using RSNA, MIMIC-CXR-JPG and JSRT datasets, demonstrating superior translation quality through lower Frechet Inception Distance (FID) and Kernel Inception Distance (KID) scores compared to existing methods (FID: 67.18 vs. 210.4, KID: 0.01604 vs. 0.225). Evaluation on RSNA opacity, MIMIC acute respiratory distress syndrome (ARDS) patient CXRs and JSRT CXRs show our method enhances segmentation accuracy of lung borders and improves lesion classification, further underscoring its potential in clinical settings (RSNA: mIoU: 76.58% vs. 62.58%, Sensitivity: 85.58% vs. 77.03%; MIMIC ARDS: mIoU: 86.20% vs. 72.07%, Sensitivity: 92.68% vs. 86.85%; JSRT: mIoU: 91.08% vs. 85.6%, Sensitivity: 97.62% vs. 95.04%). Our approach advances CXR imaging analysis, especially in investigating segmentation impacts through image translation techniques.
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