Normative representation learning focuses on understanding the typical anatomical distributions from large datasets of medical scans from healthy individuals. Generative Artificial Intelligence (AI) leverages this attribute to synthesize images that accurately reflect these normative patterns. This capability enables the AI allowing them to effectively detect and correct anomalies in new, unseen pathological data 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. In addition, we conduct a large multi-reader study to compare these metrics to experts’ evaluations. Our analysis demonstrates that models proficient in normative learning exhibit exceptional versatility, adeptly detecting a wide range of unseen medical conditions. Our code is available at
https://github.com/compai-lab/2024-ncomms-bercea.git
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Generative AI can learn normative patterns to detect unseen anomalies. The authors introduce metrics to evaluate the representation of healthy anatomy, showing that high scoring models improve detection and generalizability across brain pathologies....

Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging

10.1038/s41467-025-56321-y