Abstract
The lack of interpretability of Deep Learning models hinders their deployment in clinical contexts. Case-based explanations can be used to justify these models’ decisions and improve their trustworthiness. However, providing medical cases as explanations may threaten the privacy of patients. We propose a generative adversarial network to disentangle identity and medical features from images. Using this network, we can alter the identity of an image to anonymize it while preserving relevant explanatory features. As a proof of concept, we apply the proposed model to biometric and medical datasets, demonstrating its capacity to anonymize medical images while preserving explanatory evidence and a reasonable level of intelligibility. Finally, we demonstrate that the model is inherently capable of generating counterfactual explanations.
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Acknowledgements
This work was partially funded by the Project TAMI - Transparent Artificial Medical Intelligence (NORTE-01-0247-FEDER-045905) financed by ERDF - European Regional Fund through the North Portugal Regional Operational Program - NORTE 2020 and by the Portuguese Foundation for Science and Technology - FCT under the CMU - Portugal International Partnership, and also by the Portuguese Foundation for Science and Technology - FCT within PhD grant number SFRH/BD/139468/2018.
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Montenegro, H., Silva, W., Cardoso, J.S. (2023). Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations. In: Fragemann, J., Li, J., Liu, X., Tsaftaris, S.A., Egger, J., Kleesiek, J. (eds) Medical Applications with Disentanglements. MAD 2022. Lecture Notes in Computer Science, vol 13823. Springer, Cham. https://doi.org/10.1007/978-3-031-25046-0_4
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