  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.
