Deep learning (DL)-based solutions have been extensively researched in the medical domain in recent years, enhancing the efficacy of diagnosis, planning, and treatment. Since the usage of health-related data is strictly regulated, processing medical records outside the hospital environment for developing and using DL models demands robust data protection measures. At the same time, it can be challenging to guarantee that a DL solution delivers a minimum level of performance when being trained on secured data, without being specifically designed for the given task. Our approach uses singular value decomposition (SVD) and principal component analysis (PCA) to obfuscate the medical images before employing them in the DL analysis. The capability of DL algorithms to extract relevant information from secured data is assessed on a task of angiographic view classification based on obfuscated frames. The security level is probed by simulated artificial intelligence (AI)-based reconstruction attacks, considering two threat actors with different prior knowledge of the targeted data. The degree of privacy is quantitatively measured using similarity indices. Although a trade-off between privacy and accuracy should be considered, the proposed technique allows for training the angiographic view classifier exclusively on secured data with satisfactory performance and with no computational overhead, model adaptation, or hyperparameter tuning. While the obfuscated medical image content is well protected against human perception, the hypothetical reconstruction attack proved that it is also difficult to recover the complete information of the original frames.

使用奇异值分解（SVD）和主成分分析（PCA）对医学图像进行混淆处理，从而保护数据隐私并在深度学习分析中提取相关信息。通过模拟基于人工智能重建攻击评估安全性，并使用相似性指数定量测量隐私程度。虽然隐私和准确性之间应权衡，但提供的技术允许仅使用受保护的数据训练血管造影图像分类器，并获得令人满意的性能，而不会带来计算负担、模型适应或超参数调整。尽管混淆的医学图像内容能很好地抵护人类感知，但假设的重建攻击证明也难以恢复原始帧的完整信息。

基于深度学习和矩阵分解的隐私保护医学图像分类