Anonymizing medical case-based explanations through disentanglement
This addresses privacy issues for clinicians and researchers using AI in healthcare, though it is an incremental advance in image anonymization techniques.
The paper tackled the problem of privacy concerns preventing the sharing of medical images for case-based explanations in clinical deep learning by proposing a method to disentangle identity and medical characteristics, enabling the generation of realistic anonymized images that preserve medical content.
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.