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

Author

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  • Cosmin I. Bercea

    (Technical University of Munich (TUM)
    Helmholtz AI and Helmholtz Center Munich)

  • Benedikt Wiestler

    (TUM School of Medicine and Health
    Munich Center for Machine Learning (MCML))

  • Daniel Rueckert

    (Munich Center for Machine Learning (MCML)
    Technical University of Munich (TUM) and TUM University Hospital
    Imperial College London)

  • Julia A. Schnabel

    (Technical University of Munich (TUM)
    Helmholtz AI and Helmholtz Center Munich
    Munich Center for Machine Learning (MCML)
    King’s College London)

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 .

Suggested Citation

  • Cosmin I. Bercea & Benedikt Wiestler & Daniel Rueckert & Julia A. Schnabel, 2025. "Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56321-y
    DOI: 10.1038/s41467-025-56321-y
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    References listed on IDEAS

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    1. Seungjun Lee & Boryeong Jeong & Minjee Kim & Ryoungwoo Jang & Wooyul Paik & Jiseon Kang & Won Jung Chung & Gil-Sun Hong & Namkug Kim, 2022. "Emergency triage of brain computed tomography via anomaly detection with a deep generative model," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Yukun Zhou & Mark A. Chia & Siegfried K. Wagner & Murat S. Ayhan & Dominic J. Williamson & Robbert R. Struyven & Timing Liu & Moucheng Xu & Mateo G. Lozano & Peter Woodward-Court & Yuka Kihara & Andre, 2023. "A foundation model for generalizable disease detection from retinal images," Nature, Nature, vol. 622(7981), pages 156-163, October.
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