Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Montenegro, H (Author) Other; Silva, W (Author) Other; Jaime S Cardoso (Author) FEUP

Journal

Title: IEEE Access

Vol. 9

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00V-N77

DOI: 10.1109/access.2021.3124844

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11