在PET-CT成像中增强肝病变细分的保护隐私的联合转移学习
Rajesh Kumar1, Shaoning Zeng2, Jay Kumar3
1International Research Center for Complexity Sciences, Hangzhou International Innovation Institute, Beihang University, Hangzhou, 311115, China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, China; Department of Nuclear Medicine, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, 313001, China.
Artificial intelligence in medicine
|September 2, 2025
概括
联合转移学习 (FTL) 通过整合保护隐私的协作和预先训练的模型来增强PET-CT扫描中的肝损伤细分. 这种方法克服了数据稀缺性和异质性,提高了诊断准确性.
科学领域:
- 医学成像分析
- 医疗保健中的人工智能
- 放射学
背景情况:
- 通过PET-CT进行精确的肝损伤诊断至关重要.
- 深度学习的部署受到数据稀缺性,隐私问题和机构间的成像异质性所阻碍.
- 现有的方法难以实现多机构数据整合和隐私保护.
研究的目的:
- 提出一种新的联合转移学习 (FTL) 框架,用于在PET-CT成像中进行强大的肝病变细分.
- 提高深度学习模型的性能,同时解决数据短缺和隐私问题.
- 提高医疗成像中的AI模型的概括性和诊断准确性.
主要方法:
- 开发了一个保护隐私的FTL框架,将联合学习和自适应转移学习结合起来.
- 引入多式特征共学习区块 (FCB) 以有效地整合PET-CT特征.
- 集成的增强隐私技术,如差分隐私 (DP) 和同型加密 (HE).
主要成果:
- 与模拟多机构PET-CT数据集的基线方法相比,FTL框架显示出优异的肝病变细分性能.
- 这种方法实现了强有力的隐私保证,确保敏感的患者数据不会被共享.
- 实验证实了数据需求的减少和模型通用性的提高.
结论:
- 拟议的FTL框架有效地解决了在PET-CT上部署深度学习的挑战.
- 这种方法为多机构的医学成像分析提供了保护隐私和数据效率的解决方案.
- FTL框架代表了人工智能驱动的肝病变诊断的重大进步.
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