In the fast-evolving field of medical image analysis, Deep Learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training and inference stages, raising privacy concerns, especially in the sensitive area of medical data. To tackle these concerns, this paper proposes a novel framework that uses surrogate images for analysis, eliminating the need for plaintext images. This approach is called Frequency-domain Exchange Style Fusion (FESF). The framework includes two main components: Image Hidden Module (IHM) and Image Quality Enhancement Module~(IQEM). The~IHM performs in the frequency domain, blending the features of plaintext medical images into host medical images, and then combines this with IQEM to improve and create surrogate images effectively. During the diagnostic model training process, only surrogate images are used, enabling anonymous analysis without any plaintext data during both training and inference stages. Extensive evaluations demonstrate that our framework effectively preserves the privacy of medical images and maintains diagnostic accuracy of DL models at a relatively high level, proving its effectiveness across various datasets and DL-based models.

翻译：在快速发展的医学图像分析领域，基于深度学习的方法已取得了巨大成功。然而，这些方法在训练和推理阶段需要使用明文数据，这在医学数据这一敏感领域引发了隐私担忧。为解决这一问题，本文提出了一种新颖框架，利用替代图像进行分析，从而消除了对明文图像的需求。该方法被称为频域交换风格融合（FESF）。该框架包含两个主要组件：图像隐藏模块（IHM）和图像质量增强模块（IQEM）。IHM在频域中运行，将明文医学图像的特征融合到宿主医学图像中，随后与IQEM结合，有效生成并改善替代图像。在诊断模型训练过程中仅使用替代图像，从而在训练和推理阶段均无需明文数据即可实现匿名分析。大量评估表明，本框架能有效保护医学图像隐私，并将深度学习模型的诊断准确性维持在较高水平，充分验证了其在不同数据集和基于深度学习的模型上的有效性。