Evaluating Normative Learning in Generative AI for Robust Medical Anomaly Detection

Abstract In Generative Artificial Intelligence (AI) for medical imaging, normative learning involves training AI models on large datasets of typical images from healthy volunteers, such as MRIs or CT scans. These models acquire the distribution of normal anatomical structures, allowing them to effectively detect and correct anomalies in new, unseen pathological data. This approach allows the detection of unknown pathologies without the need for expert labeling. Traditional anomaly detection methods often evaluate the anomaly detection performance, overlooking the crucial role of normative learning. In our analysis, we introduce novel metrics, specifically designed to evaluate this facet in AI models. We apply these metrics across various generative AI frameworks, including advanced diffusion models, and rigorously test them against complex and diverse brain pathologies. Our analysis demonstrates that models proficient in normative learning exhibit exceptional versatility, adeptly detecting a wide range of unseen medical conditions.