ArticleNature communications2025
Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Unsupervised anomaly detection for tumor delineation in a preclinical model of glioblastoma using CEST MRI.bioRxiv : the preprint server for biology · 2026Article
- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.NPJ digital medicine · 2026Article
- Conditional deep generative normative modeling for structural and developmental anomaly detection in the fetal brain.NeuroImage · 2025Article
- Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging.Nature communications · 2025Article
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
- PANDA: Patch-based unsupervised deep learning for brain anomaly detection via age prediction in fetal MRI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.