Towards Privacy-preserving Explanations in Medical Image Analysis
This addresses privacy concerns for medical practitioners and patients using interpretable AI, but it is incremental as it analyzes existing methods and identifies drawbacks.
The paper tackled the problem of patient privacy threats from case-based explanations in medical image analysis by evaluating privacy-preserving methods, finding that PPRL-VGAN best preserved disease-related features while ensuring high privacy among state-of-the-art methods.
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.