Metaheuristic Optimization for Generative and Explainable AI in Biomedical Imaging

Abstract

The medical imaging field is undergoing transformation through the integration of generative AI and explainable AI (XAI), enabling advanced diagnostics and transparent decision-making. This chapter explores the synergistic integration of these AI frameworks with metaheuristic algorithms, including Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and Differential Evolution, to enhance system performance and reliability. Metaheuristic approaches address optimization challenges while augmenting Variational Autoencoders and Generative Adversarial Networks in applications from synthetic image generation to rare pathology modeling. Case studies demonstrate how metaheuristic-optimized GANs improve image quality and address class imbalance, while metaheuristic algorithms enhance interpretability mechanisms including saliency maps, SHAP, and LIME, fostering trust in AI-driven diagnostics while ensuring regulatory compliance. This integration enables biomedical imaging systems to achieve superior performance, enhanced interpretability, and ethical implementation.

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