用扩散模型生成逼真血管狭窄图像，提升小样本下的诊断准确率。
Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection
- 基于扩散模型的图像修复技术，可由用户控制狭窄程度。
- 在小样本下仍保持高检测与分类精度，优于传统方法。
- 适合医疗数据稀缺场景，助力临床决策支持系统。
冠状动脉狭窄是导致缺血性心脏病事件和死亡的重要风险因素，其诊疗依赖于耗时的医学分析。冠状动脉造影提供关键视觉线索，支持临床诊断与治疗决策。近年来深度学习在狭窄自动定位与严重程度评估方面展现出巨大潜力。然而，真实场景中受限于标注数据少和类别不平衡问题，现有方法性能受限。本文提出一种基于扩散模型的新型数据增强方法，通过图像修复生成逼真病变，支持用户引导控制病变严重程度。在多种合成数据集规模下对病变检测与严重程度分类任务的广泛评估显示，该方法在大规模院内数据集和公开冠状动脉造影数据集上均表现优异。此外，即使在有限数据训练下仍保持高检测与分类性能，凸显其在提升狭窄严重程度评估能力与优化数据利用方面的临床价值。
原文摘要 · Abstract (English)
Coronary stenosis is a major risk factor for ischemic heart events leading to increased mortality, and medical treatments for this condition require meticulous, labor-intensive analysis. Coronary angiography provides critical visual cues for assessing stenosis, supporting clinicians in making informed decisions for diagnosis and treatment. Recent advances in deep learning have shown great potential for automated localization and severity measurement of stenosis. In real-world scenarios, however, the success of these competent approaches is often hindered by challenges such as limited labeled data and class imbalance. In this study, we propose a novel data augmentation approach that uses an inpainting method based on a diffusion model to generate realistic lesions, allowing user-guided control of severity. Extensive evaluation on lesion detection and severity classification across various synthetic dataset sizes shows superior performance of our method on both a large-scale in-house dataset and a public coronary angiography dataset. Furthermore, our approach maintains high detection and classification performance even when trained with limited data, highlighting its clinical importance in improving the assessment of severity of stenosis and optimizing data utilization for more reliable decision support.
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