Case-based explanations are an intuitive method to gain insight into the decision-making process of deep learning models in clinical contexts. However, medical images cannot be shared as explanations due to privacy concerns. To address this problem, we propose a novel method for disentangling identity and medical characteristics of images and apply it to anonymize medical images. The disentanglement mechanism replaces some feature vectors in an image while ensuring that the remaining features are preserved, obtaining independent feature vectors that encode the images' identity and medical characteristics. We also propose a model to manufacture synthetic privacy-preserving identities to replace the original image's identity and achieve anonymization. The models are applied to medical and biometric datasets, demonstrating their capacity to generate realistic-looking anonymized images that preserve their original medical content. Additionally, the experiments show the network's inherent capacity to generate counterfactual images through the replacement of medical features.

翻译：案例解释是一种直观的方法，可用于深入了解临床背景下深度学习模型的决策过程。然而，由于隐私问题，医学图像不能作为解释内容共享。为解决此问题，我们提出了一种新方法，用于解耦图像的标识与医学特征，并将其应用于医学图像的匿名化。该解耦机制通过替换图像中的部分特征向量，同时确保其余特征保持不变，从而获得编码图像标识与医学特征的独立特征向量。我们进一步提出了一种模型，用于生成合成隐私保护标识以替代原始图像的标识，从而实现匿名化。该模型被应用于医学与生物特征数据集，展示了其生成保留原始医学内容、外观逼真的匿名化图像的能力。此外，实验表明，该网络通过替换医学特征，本身具备生成反事实图像的能力。