Anonymizing medical case-based explanations through disentanglement
Helena Montenegro Jaime S. Cardoso
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.