用少量肿瘤数据生成真实脑瘤图像，保护隐私并提升分割效果
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
- 通过融合健康背景与肿瘤前景生成新图像，实现隐私保护下的数据合成
- 在独立数据集上使分割Dice分数提升4.6%，公平性显著改善
- 适合医疗数据共享场景，尤其适用于小样本敏感医学图像生成
利用多中心医疗数据进行分析面临隐私担忧和数据异质性挑战。尽管分布式方法如联邦学习已广泛应用，但在医学影像等敏感领域仍易遭受隐私泄露。生成模型（如扩散模型）可通过合成真实数据增强隐私保护，但小样本训练下易产生记忆问题。本文提出去中心化少样本生成模型（DFGM），在不泄露原始数据的前提下合成脑瘤图像。该方法将各中心私有的肿瘤数据与公开的健康图像融合，通过拼接肿瘤区域与健康背景构建新数据集，既保障严格隐私，又实现可控、高质量生成。我们使用UNet评估了其在脑瘤分割中的效果，在独立数据集上，数据增强使Dice分数提升3.9%，公平性提升达4.6%。
原文摘要 · Abstract (English)
Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has gained traction, they remain vulnerable to privacy breaches, particularly in sensitive domains like medical imaging. Generative models, such as diffusion models, enhance privacy by synthesizing realistic data. However, they are prone to memorization, especially when trained on small datasets. This study proposes a decentralized few-shot generative model (DFGM) to synthesize brain tumor images while fully preserving privacy. DFGM harmonizes private tumor data with publicly shareable healthy images from multiple medical centers, constructing a new dataset by blending tumor foregrounds with healthy backgrounds. This approach ensures stringent privacy protection and enables controllable, high-quality synthesis by preserving both the healthy backgrounds and tumor foregrounds. We assess DFGM's effectiveness in brain tumor segmentation using a UNet, achieving Dice score improvements of 3.9% for data augmentation and 4.6% for fairness on a separate dataset.
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