重新思考医疗成像AI中的隐私:从元数据和像素级识别风险到联合学习和合成数据挑战
Konstantina Giouroukou1,2, Kostas Marias2,3, Manolis Tsiknakis2,3
1Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, Voutes Campus, Street Address, 71003 Heraklion, Crete, Greece.
Radiology. Artificial intelligence
|November 26, 2025
概括
在医学成像中保护患者隐私,人工智能需要消除元数据和像素级图像数据的识别. 像联合学习和合成数据这样的先进方法对复杂的攻击有局限性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 传统上,去识别的重点是医疗成像数据集中的元数据.
- 深度学习模型可以从像素级图像数据中提取敏感的患者信息.
- 这带来了超越传统元数据问题的重大隐私风险.
研究的目的:
- 讨论元数据和像素级数据作为医学成像中可识别信息的来源.
- 审查保护隐私的技术,如联合学习和合成数据生成.
- 突出这些方法在AI应用中的局限性和漏洞.
主要方法:
- 对医疗成像非识别现有文献的审查.
- 分析与元数据和内在图像数据相关的隐私风险.
- 评估联合学习和合成数据生成以保护隐私.
- 讨论诸如模型倒置和推理攻击之类的漏洞.
主要成果:
- 元数据和像素级图像数据都包含可识别的信息.
- 联合学习和合成数据生成提供隐私优势,但并非无可击.
- 模型逆转和推理攻击仍然是对隐私的重大威胁.
结论:
- 综合性去识别策略必须涉及元数据和图像内容.
- 当前隐私保护方法的局限性需要在人工智能部署中仔细考虑.
- 持续的研究对于减轻医学成像AI中的高级隐私风险至关重要.
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