Towards All-in-One Medical Image Re-Identification

Abstract

Medical image re-identification (MedReID) is under-explored so far, despite its critical applications in personalized healthcare and privacy protection.In this paper, we introduce a thorough benchmark and a unified model for this problem.First, to handle various medical modalities, we propose a novel Continuous Modality-based Parameter Adapter (ComPA). ComPA condenses medical content into a continuous modality representation and dynamically adjusts the modality-agnostic model with modality-specific parameters at runtime. This allows a single model to adaptively learn and process diverse modality data.Furthermore, we integrate medical priors into our model by aligning it with a bag of pre-trained medical foundation models, in terms of the differential features.Compared to single-image feature, modeling the inter-image difference better fits the re-identification problem, which involves discriminating multiple images.We evaluate the proposed model against 25 foundation models and 8 large multi-modal language models across 11 image datasets, demonstrating consistently superior performance.Additionally, we deploy the proposed MedReID technique to two real-world applications, i.e., history-augmented personalized diagnosis and medical privacy protection.