Privacy Preserved and Explainable Deep Medical Image Analysis: A Survey
Abstract
Deep neural networks play a significant role in medical image analysis, particularly in improving the efficiency and accuracy of disease diagnosis and treatment planning. The ability to preserve the privacy of medical data opens the door to harnessing more information to train powerful and intelligent AI models. However, the added complexity introduced by incorporating privacy measures often leads to increasingly opaque deep learning models. As the need to interpret these privacy-preserving deep learning models grows, explainable AI systems have become pivotal for cultivating trust among clinical experts and stakeholders. To address this challenge, researchers have begun to focus on developing privacy-preserving techniques with improved explainability features. This article presents a comprehensive survey of different privacy models and security techniques, focusing on biomedical imaging applications. The survey covers peer-reviewed studies published during 2019–2024, ensuring comprehensive and contemporary coverage of privacy-preserving and explainability techniques. It covers a detailed review of various privacy-preserving methods and studies related to model explanations. It also highlights unresolved challenges and suggests potential research directions. This survey aims to offer valuable directions to the research community by explaining privacy-preserving techniques for medical image analysis.
