Safe Medical AI: Counterfactual Testing of Chest X-Ray Explanations

This project evaluates the stability of explainable AI methods used in chest X-ray segmentation models. We apply controlled, synthetic boundary perturbations to test how Grad-CAM and related attribution methods change under realistic variations. The goal is to identify reliable explanation techniques that support safer clinical deployment of medical AI systems.