提出频率校准方法，提升医疗图像生成的可靠性
Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Method
- 通过高频成分替换与重建，校准真实与生成图像的频域分布
- 在脑部MRI、胸部X光等数据上显著提升分类准确率
- 可独立使用且适配任意生成模型，适合医疗AI数据增强场景
医疗AI发展依赖大规模数据，但常面临数据稀缺问题。利用生成模型进行图像数据增强（GDA）可合成真实医学影像，但其偏差常被低估，存在引入有害特征的风险。本文发现真实与生成图像间的频率分布不一致是导致GDA不可靠的关键因素，提出频率校准（FreRec）方法以减少这种差异。FreRec包含：(1) 统计高频替换（SHR），粗略对齐高频分量；(2) 重建式高频映射（RHM），提升图像质量并恢复高频细节。在脑部MRI、胸部X光和眼底图像等多个医疗数据集上进行大量实验，结果表明，与未校准的生成样本相比，FreRec显著提升了下游医学图像分类性能。该方法为独立后处理步骤，兼容任意生成模型，可无缝集成至常见医疗GDA流程中。
原文摘要 · Abstract (English)
Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize realistic medical images. However, the bias in GDA is often underestimated in medical domains, with concerns about the risk of introducing detrimental features generated by AI and harming downstream tasks. This paper identifies the frequency misalignment between real and synthesized images as one of the key factors underlying unreliable GDA and proposes the Frequency Recalibration (FreRec) method to reduce the frequency distributional discrepancy and thus improve GDA. FreRec involves (1) Statistical High-frequency Replacement (SHR) to roughly align high-frequency components and (2) Reconstructive High-frequency Mapping (RHM) to enhance image quality and reconstruct high-frequency details. Extensive experiments were conducted in various medical datasets, including brain MRIs, chest X-rays, and fundus images. The results show that FreRec significantly improves downstream medical image classification performance compared to uncalibrated AI-synthesized samples. FreRec is a standalone post-processing step that is compatible with any generative model and can integrate seamlessly with common medical GDA pipelines.
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