Towards All-in-One Medical Image Re-Identification

• URL: http://arxiv.org/abs/2503.08173v1
• Date: Tue, 11 Mar 2025 08:35:00 GMT
• Title: Towards All-in-One Medical Image Re-Identification
• Authors: Yuan Tian, Kaiyuan Ji, Rongzhao Zhang, Yankai Jiang, Chunyi Li, Xiaosong Wang, Guangtao Zhai,
• Abstract summary: Medical image re-identification (MedReID) is under-explored so far, despite its critical applications in personalized healthcare and privacy protection.<n>We introduce a thorough benchmark and a unified model for this problem.<n>We deploy the proposed MedReID technique to two real-world applications, history-augmented personalized diagnosis and medical privacy protection.
• Score: 34.74569001275221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Codes and model is available at \href{https://github.com/tianyuan168326/All-in-One-MedReID-Pytorch}{https://github.com/tianyuan168326/All-in-One-MedReID-Pytorch}.

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