用扩散模型生成超声图像，提升胎儿切面分类准确率
Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models
- 用扩散模型生成合成超声图像补充数据
- 结合真实数据微调后分类准确率更高
- 适合解决医学影像数据稀缺问题
超声成像广泛应用于医疗诊断，尤其在胎儿健康评估中。然而高质量标注的超声图像数量有限，制约了机器学习模型的训练。本文研究利用扩散模型生成合成超声图像，以提升胎儿切面分类性能。先在合成图像上训练分类器，再用真实图像进行微调。大量实验表明，将生成图像纳入训练流程，分类准确率优于仅使用真实图像训练。结果表明，利用扩散模型生成合成数据，是缓解超声医学影像数据稀缺问题的有效手段。
原文摘要 · Abstract (English)
Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultrasound images to improve the performance on fetal plane classification. We train different classifiers first on synthetic images and then fine-tune them with real images. Extensive experimental results demonstrate that incorporating generated images into training pipelines leads to better classification accuracy than training with real images alone. The findings suggest that generating synthetic data using diffusion models can be a valuable tool in overcoming the challenges of data scarcity in ultrasound medical imaging.
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