GA-RISE: Posthoc Model Agnostic Explanations of Black-Box Classifiers Using Genetic Algorithm-Based Optimized Masks — A Case Study on Chest X-ray Images

The intricate and recursive nature of deep learning models often obscures their inner workings, limiting their applications in high-risk domains like medical image analysis. The development of Explainable AI (XAI) methods has addressed this limitation by providing human-readable insights into these black-box models, which are widely used in image-based tasks such as classification, segmentation, and localization. One popular and effective XAI technique is Randomized Input Sampling for Explanation (RISE), which uses a large number of random masks to visualize model behavior. However, RISE’s requirement for numerous masks leads to high space complexity. In this paper, we propose a modified RISE technique that optimizes the use of a relatively small number of masks through a Genetic Algorithm to enhance comprehensibility. Experiments with state-of-the-art classifier models on a publicly available Chest X-ray dataset demonstrate that our approach outperforms other commonly used post hoc model-agnostic and non-model-agnostic techniques in terms of explanation quality and faithfulness metrics.