Anonymizing medical case-based explanations through disentanglement

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Montenegro, H (Author) Other; Jaime S Cardoso (Author) FEUP

Journal

Title: Medical Image Analysis

Vol. 95

ISSN: 1361-8415

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-MX8

DOI: 10.1016/j.media.2024.103209

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12