标题： 魔鬼藏在提示中：去识别化痕迹增强合成胸部X光生成的记忆风险 显示英文标题

标题： The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation

摘要： 生成模型，尤其是文本到图像（T2I）扩散模型，在医学图像分析中扮演着关键角色。然而，这些模型容易受到训练数据记忆的影响，从而对患者隐私构成重大风险。合成胸部X射线生成是医学图像分析中最常见的应用之一，MIMIC-CXR数据集作为该任务的主要数据存储库。本研究首次系统地尝试识别在MIMIC-CXR中对训练数据记忆贡献最大的提示和文本标记。我们的分析揭示了两个意外发现：(1) 包含去标识化程序痕迹（用于隐藏受保护健康信息的标记）的提示是最容易被记忆的，(2) 在所有标记中，去标识化标记对记忆贡献最大。这突显了标准匿名化实践与使用MIMIC-CXR的T2I合成中的一个更广泛问题。此外，现有的推理阶段记忆缓解策略无效，无法充分减少模型对记忆文本标记的依赖。在这方面，我们为不同利益相关者提出了可操作的策略，以增强隐私并提高医学成像中生成模型的可靠性。最后，我们的结果为未来基于MIMIC-CXR数据集开发和基准测试合成胸部X射线生成的记忆缓解技术奠定了基础。匿名代码可在https://anonymous.4open.science/r/diffusion_memorization-8011/ 获取。 显示英文摘要

摘要： Generative models, particularly text-to-image (T2I) diffusion models, play a crucial role in medical image analysis. However, these models are prone to training data memorization, posing significant risks to patient privacy. Synthetic chest X-ray generation is one of the most common applications in medical image analysis with the MIMIC-CXR dataset serving as the primary data repository for this task. This study presents the first systematic attempt to identify prompts and text tokens in MIMIC-CXR that contribute the most to training data memorization. Our analysis reveals two unexpected findings: (1) prompts containing traces of de-identification procedures (markers introduced to hide Protected Health Information) are the most memorized, and (2) among all tokens, de-identification markers contribute the most towards memorization. This highlights a broader issue with the standard anonymization practices and T2I synthesis with MIMIC-CXR. To exacerbate, existing inference-time memorization mitigation strategies are ineffective and fail to sufficiently reduce the model's reliance on memorized text tokens. On this front, we propose actionable strategies for different stakeholders to enhance privacy and improve the reliability of generative models in medical imaging. Finally, our results provide a foundation for future work on developing and benchmarking memorization mitigation techniques for synthetic chest X-ray generation using the MIMIC-CXR dataset. The anonymized code is available at https://anonymous.4open.science/r/diffusion_memorization-8011/