提出OKRA框架，实现医疗影像的高效隐私保护学习
Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
- 基于正交基帧的随机编码，提升核方法在分布式数据上的计算效率
- 在多个临床影像数据集上，精度与速度均优于现有最佳方案
- 适合需要保护患者隐私的医院间协作式医疗AI开发
医学影像在现代医疗中至关重要，从磁共振成像（MRI）到血细胞显微检测，为疾病诊断和个性化治疗提供关键信息。尽管核方法广泛应用于机器学习，但其应用面临两大挑战：医疗影像数据通常来自不同医院，因隐私问题无法集中；且图像数据维度高，计算复杂。虽然随机编码被视为有前景的方向，但现有方法常在精度与效率间难以平衡。为此，本文提出一种基于随机编码的新型核学习方法OKRA（Orthonormal K-fRAmes），专为常用核函数设计，显著提升可扩展性与运行速度。在多个临床影像数据集上的实验表明，该方法在模型质量、计算性能和资源开销方面均优于当前最优方案。
原文摘要 · Abstract (English)
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights into patient health. To predict diseases or provide individualized therapies, machine learning techniques like kernel methods have been widely used. Nevertheless, there are multiple challenges for implementing kernel methods. Medical image data often originates from various hospitals and cannot be combined due to privacy concerns, and the high dimensionality of image data presents another significant obstacle. While randomised encoding offers a promising direction, existing methods often struggle with a trade-off between accuracy and efficiency. Addressing the need for efficient privacy-preserving methods on distributed image data, we introduce OKRA (Orthonormal K-fRAmes), a novel randomized encoding-based approach for kernel-based machine learning. This technique, tailored for widely used kernel functions, significantly enhances scalability and speed compared to current state-of-the-art solutions. Through experiments conducted on various clinical image datasets, we evaluated model quality, computational performance, and resource overhead. Additionally, our method outperforms comparable approaches
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