用扩散模型生成犬类心脏病的合成X光片提升检测精度
Confident Pseudo-labeled Diffusion Augmentation for Canine Cardiomegaly Detection
- 用扩散模型生成带关键点标注的合成犬心超声图像
- 通过蒙特卡洛丢弃筛选高置信度伪标签，迭代优化数据集
- 在小样本下实现当前最优检测效果，适合医疗影像研究者
犬类心脏扩大症若未被及时发现将带来严重健康风险，需精准诊断方法。现有检测模型多依赖规模小且标注不佳的数据集，在不同成像条件下泛化能力差，限制了实际应用。为此，我们提出自信伪标签扩散增强（CDA）模型，用于识别犬类心脏扩大症。针对高质量训练数据有限的问题，该方法利用扩散模型生成合成X光图像，并自动标注椎骨心率关键点，从而扩充数据集。同时，采用蒙特卡洛丢弃的伪标签策略，筛选高置信度标签，精炼合成数据并提升模型准确率。通过迭代引入这些标签，显著提升模型性能，超越传统方法，在犬类心脏扩大症检测中达到当前最优水平。代码已开源：https://github.com/Shira7z/CDA。
原文摘要 · Abstract (English)
Canine cardiomegaly, marked by an enlarged heart, poses serious health risks if undetected, requiring accurate diagnostic methods. Current detection models often rely on small, poorly annotated datasets and struggle to generalize across diverse imaging conditions, limiting their real-world applicability. To address these issues, we propose a Confident Pseudo-labeled Diffusion Augmentation (CDA) model for identifying canine cardiomegaly. Our approach addresses the challenge of limited high-quality training data by employing diffusion models to generate synthetic X-ray images and annotate Vertebral Heart Score key points, thereby expanding the dataset. We also employ a pseudo-labeling strategy with Monte Carlo Dropout to select high-confidence labels, refine the synthetic dataset, and improve accuracy. Iteratively incorporating these labels enhances the model's performance, overcoming the limitations of existing approaches. Experimental results show that the CDA model outperforms traditional methods, achieving state-of-the-art accuracy in canine cardiomegaly detection. The code implementation is available at https://github.com/Shira7z/CDA.
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