Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging
Abstract 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 patter...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56321-y |
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| _version_ | 1850197960875311104 |
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| author | Cosmin I. Bercea Benedikt Wiestler Daniel Rueckert Julia A. Schnabel |
| author_facet | Cosmin I. Bercea Benedikt Wiestler Daniel Rueckert Julia A. Schnabel |
| author_sort | Cosmin I. Bercea |
| collection | DOAJ |
| description | Abstract 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 . |
| format | Article |
| id | doaj-art-3e51bfb18ec749f28155672166d0f3d5 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3e51bfb18ec749f28155672166d0f3d52025-08-20T02:12:59ZengNature PortfolioNature Communications2041-17232025-02-0116111010.1038/s41467-025-56321-yEvaluating normative representation learning in generative AI for robust anomaly detection in brain imagingCosmin I. Bercea0Benedikt Wiestler1Daniel Rueckert2Julia A. Schnabel3Chair of Computational Imaging and AI in Medicine, Technical University of Munich (TUM)Chair of AI for Image-Guided Diagnosis and Therapy, TUM School of Medicine and HealthMunich Center for Machine Learning (MCML)Chair of Computational Imaging and AI in Medicine, Technical University of Munich (TUM)Abstract 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 .https://doi.org/10.1038/s41467-025-56321-y |
| spellingShingle | Cosmin I. Bercea Benedikt Wiestler Daniel Rueckert Julia A. Schnabel Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging Nature Communications |
| title | Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging |
| title_full | Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging |
| title_fullStr | Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging |
| title_full_unstemmed | Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging |
| title_short | Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging |
| title_sort | evaluating normative representation learning in generative ai for robust anomaly detection in brain imaging |
| url | https://doi.org/10.1038/s41467-025-56321-y |
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