Evolving Visual Counterfactual Medical Imagery Explanations with Cooperative Co-evolution using Dynamic Decomposition

Explainable AI (XAI) is becoming increasingly vital in healthcare, particularly in understanding decisions made by complex, black-box models in medical diagnostics. Amid discussions on balancing AI's predictive accuracy with interpretability, 'post-hoc' XAI methods are a potential solution, offering interpretability after model training without compromising diagnostic precision. Counterfactual explanations are one such 'post-hoc' XAI technique, where these explanations modify input data, like features in an X-ray image, to see how such changes affect AI image-driven diagnoses, offering insights into the model's reasoning. However, the complexity of medical image data poses challenges in generating these counterfactuals. To address this challenge, we propose a novel Cooperative Co-evolution approach with dynamic decomposition for explainability in AI for medical diagnostics by evolving visual counterfactual explanations for medical images without requiring preliminary assumptions about image decomposition. The method, evaluated using MNIST and Diabetic Foot Osteomyelitis datasets and benchmarked against a genetic algorithm, was capable of generating counterfactual explanations for medical images, showing advantages with larger images.