Semantic versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification

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 that comprises two steps: (1) \textbf{Identity-Blocking}, which blocks varying proportions of identity-related regions, to achieve different privacy levels; and (2) \textbf{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 \textbf{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, demonstrating our state-of-the-art performance.