Open AccessPrivacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis
Author(s) -
Helena Montenegro,
Wilson Silva,
Jaime S. Cardoso
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3124844
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.