用合成胸片训练模型，高效检测冠状动脉钙化。
Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays
- 用CT生成合成胸片（DRR），带精确标签用于训练
- 轻量CNN+超分增强使检测AUC达0.754，优于已有研究
- 适合心血管筛查场景，为真实胸片迁移打基础
冠状动脉钙化（CAC）是心血管事件的重要预测指标，临床金标准为基于CT的Agatston评分。但CT成本高、难用于大规模筛查；胸片（CXRs）便宜，却缺乏可靠标注，制约深度学习发展。数字重建投影图（DRRs）通过将CT体积投影为类似胸片的图像，继承精确标注，提供可扩展的替代方案。本研究首次系统评估DRRs作为CAC检测训练域的可行性。基于COCA数据集667例CT扫描生成合成DRRs，评估模型容量、超分辨率保真度提升、预处理与训练策略。结果表明：从零训练的轻量CNN优于大型预训练网络；超分辨率结合对比度增强显著提升性能；课程学习在弱监督下稳定训练。最佳配置平均AUC达0.754，达到或超过以往基于真实胸片的研究。该成果确立了DRRs作为可扩展、标签丰富的CAC检测基础，并为未来向真实胸片的迁移学习与领域自适应奠定基础。
原文摘要 · Abstract (English)
Coronary artery calcification (CAC) is a strong predictor of cardiovascular events, with CT-based Agatston scoring widely regarded as the clinical gold standard. However, CT is costly and impractical for large-scale screening, while chest X-rays (CXRs) are inexpensive but lack reliable ground truth labels, constraining deep learning development. Digitally reconstructed radiographs (DRRs) offer a scalable alternative by projecting CT volumes into CXR-like images while inheriting precise labels. In this work, we provide the first systematic evaluation of DRRs as a surrogate training domain for CAC detection. Using 667 CT scans from the COCA dataset, we generate synthetic DRRs and assess model capacity, super-resolution fidelity enhancement, preprocessing, and training strategies. Lightweight CNNs trained from scratch outperform large pretrained networks; pairing super-resolution with contrast enhancement yields significant gains; and curriculum learning stabilises training under weak supervision. Our best configuration achieves a mean AUC of 0.754, comparable to or exceeding prior CXR-based studies. These results establish DRRs as a scalable, label-rich foundation for CAC detection, while laying the foundation for future transfer learning and domain adaptation to real CXRs.
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