综述医疗影像分析中的隐私保护技术及其应用
Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications
- 梳理加密、差分隐私、联邦学习等隐私保护方法
- 覆盖诊断、病理、远程医疗等典型应用场景
- 适合关注医疗AI隐私安全的研究者与从业者
随着人工智能和深度学习的快速发展，医学图像分析已成为现代医疗中提升诊断准确性和效率的关键工具。然而，基于AI的方法也引发严重隐私问题，因为医学图像通常包含高度敏感的患者信息。本文综述了医学图像分析中的隐私保护技术，包括加密、差分隐私、同态加密、联邦学习以及生成对抗网络。我们探讨了这些技术在诊断、病理分析和远程医疗等任务中的应用。特别地，我们根据具体挑战及其对应解决方案组织综述内容，使技术应用与实际问题直接对齐，填补当前研究空白。此外，还讨论了零知识证明和安全多方计算等新兴趋势，为未来研究提供洞见。本综述为研究人员和实践者提供了宝贵资源，有助于推动医学图像分析中的隐私保护发展。
原文摘要 · Abstract (English)
With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.
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