Anonymizing medical case-based explanations through disentanglement.

Autor: Montenegro H; Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal; INESC TEC, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal. Electronic address: maria.h.sampaio@inesctec.pt., Cardoso JS; Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal; INESC TEC, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2024 Jul; Vol. 95, pp. 103209. Date of Electronic Publication: 2024 May 17.
DOI: 10.1016/j.media.2024.103209
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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