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The healthcare industry faces significant challenges in leveraging patient data across institutions while maintaining privacy, particularly when third-party organizations like insurance companies and banks require medical information for risk assessment. The rapid advancement of large-scale multimodal models, such as Contrastive Language-Image Pre-training (CLIP), holds immense potential for medical applications by enabling cross-modal alignment of visual and textual data. This paper presents a novel framework that combines vertical federated learning with CLIP to enable privacy-preserving medical image analysis across institutional boundaries. Our framework allows secure analysis of distributed medical data without raw data sharing, while optimizing CLIP’s performance for medical applications through Context Optimization. Experimental validation on a dataset of 7023 brain MRI scans demonstrates the framework’s effectiveness, achieving 93.1% accuracy in classifying four types of brain conditions (glioma, meningioma, pituitary, and no tumor) - a substantial improvement from the original pre-trained CLIP model’s 26.3% accuracy. These results establish a practical solution for secure, cross-institutional medical data analysis that maintains patient privacy while enabling critical business decisions in healthcare, insurance, and financial sectors.

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A Privacy-Preserving Framework for Cross-Institutional Medical Image Analysis Using Vision-Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Yang Jiafeng,
  • Zhukova Natalia Alexandrovna,
  • Stankova Elena Nikolaevna

摘要

The healthcare industry faces significant challenges in leveraging patient data across institutions while maintaining privacy, particularly when third-party organizations like insurance companies and banks require medical information for risk assessment. The rapid advancement of large-scale multimodal models, such as Contrastive Language-Image Pre-training (CLIP), holds immense potential for medical applications by enabling cross-modal alignment of visual and textual data. This paper presents a novel framework that combines vertical federated learning with CLIP to enable privacy-preserving medical image analysis across institutional boundaries. Our framework allows secure analysis of distributed medical data without raw data sharing, while optimizing CLIP’s performance for medical applications through Context Optimization. Experimental validation on a dataset of 7023 brain MRI scans demonstrates the framework’s effectiveness, achieving 93.1% accuracy in classifying four types of brain conditions (glioma, meningioma, pituitary, and no tumor) - a substantial improvement from the original pre-trained CLIP model’s 26.3% accuracy. These results establish a practical solution for secure, cross-institutional medical data analysis that maintains patient privacy while enabling critical business decisions in healthcare, insurance, and financial sectors.