Towards Privacy-preserving Explanations in Medical Image Analysis

• URL: http://arxiv.org/abs/2107.09652v1
• Date: Tue, 20 Jul 2021 17:35:36 GMT
• Title: Towards Privacy-preserving Explanations in Medical Image Analysis
• Authors: H. Montenegro, W. Silva, J. S. Cardoso
• Abstract summary: The PPRL-VGAN deep learning method was the best at preserving the disease-related semantic features while guaranteeing a high level of privacy. We emphasize the need to improve privacy-preserving methods for medical imaging.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of Deep Learning in the medical field is hindered by the lack of interpretability. Case-based interpretability strategies can provide intuitive explanations for deep learning models' decisions, thus, enhancing trust. However, the resulting explanations threaten patient privacy, motivating the development of privacy-preserving methods compatible with the specifics of medical data. In this work, we analyze existing privacy-preserving methods and their respective capacity to anonymize medical data while preserving disease-related semantic features. We find that the PPRL-VGAN deep learning method was the best at preserving the disease-related semantic features while guaranteeing a high level of privacy among the compared state-of-the-art methods. Nevertheless, we emphasize the need to improve privacy-preserving methods for medical imaging, as we identified relevant drawbacks in all existing privacy-preserving approaches.

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