医学生成模型中的患者隐私审计：基于DeepSSIM++的可扩展记忆检测

中文摘要 AI 辅助

深度生成模型为医学图像合成与数据共享提供了新机遇，但它们记忆并复现训练样本的能力引发了患者隐私的严重担忧。大规模检测此类记忆行为颇具挑战：传统基于像素的指标对生成伪影敏感，而通用嵌入指标往往缺乏医学数据所需的解剖学敏感性。为应对这一挑战，我们提出DeepSSIM++——一种用于医学生成模型可扩展记忆审计的自监督相似度指标。通过利用多尺度特征聚合与保留解剖结构的增强技术，DeepSSIM++学习到一个嵌入空间，其中余弦相似度近似结构相似性指数（SSIM），无需精确的像素级配准。与最先进的基线方法相比，DeepSSIM++在理想配准下实现了平均Macro F1值提升33个百分点，在现实空间与强度扰动下提升46个百分点；此外，与解析型SSIM相比，它将大规模相似度计算提速数个数量级。DeepSSIM++兼具解剖学敏感性与计算效率，为医学生成AI提供了可扩展记忆审计的开源工具，代码与数据可在该公开URL获取。

英文摘要

While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samples raises serious concerns about patient confidentiality. Detecting such memorization at scale remains challenging: traditional pixel-based metrics are sensitive to generation artifacts, whereas generic embedding-based metrics often lack the anatomical sensitivity required for medical data. To address this challenge, we introduce DeepSSIM++, a self-supervised similarity metric for scalable memorization auditing in medical generative models. By leveraging multi-scale feature aggregation and anatomy-preserving augmentations, DeepSSIM++ learns an embedding space where cosine similarity approximates the Structural Similarity Index (SSIM), eliminating the need for exact pixel-level registration. Compared with state-of-the-art baselines, DeepSSIM++ achieves an average Macro F1 improvement of 33 percentage points under ideal alignment and 46 percentage points under realistic spatial and intensity perturbations. Furthermore, it accelerates large-scale similarity computation by several orders of magnitude compared with analytical SSIM. By combining anatomical sensitivity and computational efficiency, DeepSSIM++ provides an open-source tool for scalable memorization auditing in medical generative AI. Code and data are publicly available at: https://github.com/brAIn-science/DeepSSIM.

发表机构

• University of Catania（卡塔尼亚大学）
• University of Messina（墨西拿大学）

机构由 AI 辅助整理，请以论文原文为准。