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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