XAI-Driven Diagnosis of Generalization Failure in State-Space Cerebrovascular Segmentation Models: A Case Study on Domain Shift Between RSNA and TopCoW Datasets

This article focuses on the application of Explainable AI (XAI) to understand and address the problem of generalization failure in medical image analysis models, specifically in the context of cerebrovascular segmentation. The study investigates the impact of domain shift (differences between datasets) on model performance and uses XAI techniques to identify the reasons behind these failures. The use of XAI is crucial for building trust and improving the reliability of AI systems in medical applications.