Abstract
Medical imaging has significantly advanced computer-aided diagnosis, yet its re-identification (ReID) risks raise critical privacy concerns, calling for de-identification (DeID) techniques. Unfortunately, existing DeID methods neither particularly preserve medical semantics, nor are flexibly adjustable towards different privacy levels. To address these issues, we propose a divide-and-conquer framework comprising two steps: (1) Identity-Blocking, which blocks varying proportions of identity-related regions, to achieve different privacy levels; and (2) Medical-Semantics-Compensation, which leverages pre-trained Medical Foundation Models (MFMs) to extract medical semantic features to compensate the blocked regions. Moreover, recognizing that features from MFMs may still contain residual identity information, we introduce a Minimum Description Length principle-based feature decoupling strategy, to effectively decouple and discard such identity components. Extensive evaluations against existing approaches across seven datasets and three downstream tasks, demonstrates our state-of-the-art performance.
Abstract (translated)
医学影像技术在计算机辅助诊断方面取得了显著进展，然而其重新识别（ReID）风险引发了重要的隐私问题，因此需要采用脱敏（DeID）技术。不幸的是，现有的DeID方法既不能特别保留医疗语义信息，也不能灵活调整以适应不同的隐私级别需求。为了解决这些问题，我们提出了一种分而治之框架，包括两个步骤：(1) 身份屏蔽，通过遮蔽不同比例的身份相关区域来实现不同程度的隐私保护；(2) 医学语义补偿，利用预训练的医学基础模型（MFMs）提取医学语义特征以补充被屏蔽的区域。此外，考虑到来自MFM的特征可能仍包含残留的身份信息，我们引入了一种基于最小描述长度原理的特征解耦策略，有效分离并丢弃这些身份成分。在七组数据集和三种下游任务上的广泛评估显示了我们的方法处于业界领先水平。
URL
https://arxiv.org/abs/2507.21703
