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• W3210588961 abstract "Although Deep Learning models have achieved incredible results in medical image classification tasks, their lack of interpretability hinders their deployment in the clinical context. Case-based interpretability provides intuitive explanations, as it is a much more human-like approach than saliency-map-based interpretability. Nonetheless, since one is dealing with sensitive visual data, there is a high risk of exposing personal identity, threatening the individuals’ privacy. In this work, we propose a privacy-preserving generative adversarial network for the privatization of case-based explanations. We address the weaknesses of current privacy-preserving methods for visual data from three perspectives: realism, privacy, and explanatory value. We also introduce a counterfactual module in our Generative Adversarial Network that provides counterfactual case-based explanations in addition to standard factual explanations. Experiments were performed in a biometric and medical dataset, demonstrating the network’s potential to preserve the privacy of all subjects and keep its explanatory evidence while also maintaining a decent level of intelligibility." @default.
• W3210588961 created "2021-11-08" @default.
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• W3210588961 date "2021-01-01" @default.
• W3210588961 modified "2023-10-01" @default.
• W3210588961 title "Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis" @default.
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• W3210588961 doi "https://doi.org/10.1109/access.2021.3124844" @default.
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