{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T20:13:26Z","timestamp":1784232806940,"version":"3.55.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Forschung, Technologie und Raumfahrt","doi-asserted-by":"publisher","award":["01IS18039A"],"award-info":[{"award-number":["01IS18039A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Forschung, Technologie und Raumfahrt","doi-asserted-by":"publisher","award":["FKZ INST 37\/1057-1 FUGG"],"award-info":[{"award-number":["FKZ INST 37\/1057-1 FUGG"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["513851350"],"award-info":[{"award-number":["513851350"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["390727645"],"award-info":[{"award-number":["390727645"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2064\/1"],"award-info":[{"award-number":["EXC 2064\/1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.media.2025.103895","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T07:54:00Z","timestamp":1765180440000},"page":"103895","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Unsupervised anomaly detection in medical imaging using aggregated normative diffusion"],"prefix":"10.1016","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8893-9817","authenticated-orcid":false,"given":"Alexander","family":"Frotscher","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2339-1183","authenticated-orcid":false,"given":"Jaivardhan","family":"Kapoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7693-0621","authenticated-orcid":false,"given":"Thomas","family":"Wolfers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3629-4384","authenticated-orcid":false,"given":"Christian F.","family":"Baumgartner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2025.103895_bib0001","unstructured":"Baid, U., et al., 2021. The RSNA-ASNR-MICCAI braTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv: 2107.02314."},{"key":"10.1016\/j.media.2025.103895_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101952","article-title":"Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study","volume":"69","author":"Baur","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103895_bib0003","series-title":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","article-title":"Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain mri","author":"Baur","year":"2020"},{"key":"10.1016\/j.media.2025.103895_bib0004","series-title":"Medical Imaging with Deep Learning","article-title":"Combining reconstruction-based unsupervised anomaly detection with supervised segmentation for brain MRIs","author":"Behrendt","year":"2024"},{"key":"10.1016\/j.media.2025.103895_bib0005","series-title":"ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)","article-title":"Mask, stitch, and re-sample: enhancing robustness and generalizability in anomaly detection through automatic diffusion models","author":"Bercea","year":"2023"},{"key":"10.1016\/j.media.2025.103895_bib0006","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"293","article-title":"Reversing the abnormal: pseudo-healthy generative networks for anomaly detection","author":"Bercea","year":"2023"},{"key":"10.1016\/j.media.2025.103895_bib0007","series-title":"MIDL","article-title":"Generalizing unsupervised anomaly detection: towards unbiased pathology screening","author":"Bercea","year":"2023"},{"issue":"3","key":"10.1016\/j.media.2025.103895_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2011.07.015","article-title":"A review of atlas-based segmentation for magnetic resonance brain images","volume":"104","author":"Cabezas","year":"2011","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2025.103895_bib0009","series-title":"MIDL","article-title":"Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders","author":"Chen","year":"2018"},{"key":"10.1016\/j.media.2025.103895_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102208","article-title":"Normative ascent with local gaussians for unsupervised lesion detection","volume":"74","author":"Chen","year":"2021","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.media.2025.103895_bib0011","article-title":"Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure","volume":"8","author":"Commowick","year":"2018","journal-title":"Nat. Scient. Rep."},{"key":"10.1016\/j.media.2025.103895_bib0012","series-title":"International MICCAI Brainlesion Workshop","first-page":"25","article-title":"Transformer based models for unsupervised anomaly segmentation in brain MR images","author":"Ghorbel","year":"2022"},{"key":"10.1016\/j.media.2025.103895_bib0013","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014. Generative adversarial nets. In: Advances in neural information processing systems 27."},{"issue":"1","key":"10.1016\/j.media.2025.103895_bib0014","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-022-01875-5","article-title":"Isles 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset","volume":"9","author":"Hernandez Petzsche","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.media.2025.103895_bib0015","series-title":"Advances in Neural Information Processing Systems 33","article-title":"Denoising diffusion probabilistic models","author":"Ho","year":"2020"},{"key":"10.1016\/j.media.2025.103895_bib0016","series-title":"MIDL","article-title":"Denoising autoencoders for unsupervised anomaly detection in brain MRI","author":"Kascenas","year":"2022"},{"key":"10.1016\/j.media.2025.103895_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102963","article-title":"The role of noise in denoising models for anomaly detection in medical images","volume":"90","author":"Kascenas","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103895_bib0018","unstructured":"Kingma, D. P., Welling, M., 2013. Auto-encoding variational bayes. arXiv: 1312.6114."},{"key":"10.1016\/j.media.2025.103895_bib0019","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"339","article-title":"Itermask2: iterative unsupervised anomaly segmentation via spatial and frequency masking for brain lesions in MRI","author":"Liang","year":"2024"},{"issue":"1","key":"10.1016\/j.media.2025.103895_bib0020","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1038\/s41597-022-01401-7","article-title":"A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms","volume":"9","author":"Liew","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.media.2025.103895_bib0021","series-title":"MICCAI 2014","article-title":"Low-rank to the rescue\u2013atlas-based analyses in the presence of pathologies","author":"Liu","year":"2014"},{"key":"10.1016\/j.media.2025.103895_bib0022","unstructured":"Malinin, A., Band, N., Ganshin, A., Chesnokov, G., Gal, Y., Gales, M. J. F., Noskov, A., Ploskonosov, A., Prokhorenkova, L., Provilkov, I., Raina, V., Raina, V., Roginskiy, D., Shmatova, M., Tigar, P., Yangel, B., 2021. Shifts: a dataset of real distributional shift across multiple large-scale tasks. arXiv: 2107.07455."},{"key":"10.1016\/j.media.2025.103895_bib0023","series-title":"International MICCAI Brain Lesion Workshop","article-title":"Challenging current semi-supervised anomaly segmentation methods for brain MRI","author":"Meissen","year":"2021"},{"key":"10.1016\/j.media.2025.103895_bib0024","series-title":"Proceedings\/IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE International Symposium on Biomedical Imaging","article-title":"Disyre: diffusion-inspired synthetic restoration for unsupervised anomaly detection","author":"Naval Marimont","year":"2024"},{"issue":"2","key":"10.1016\/j.media.2025.103895_bib0025","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/42.836373","article-title":"New variants of a method of MRI scale standardization","volume":"19","author":"Ny\u00fal","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103895_bib0026","series-title":"Computer Graphics and Interactive Techniques","first-page":"681","article-title":"Improving noise","author":"Perlin","year":"2002"},{"key":"10.1016\/j.media.2025.103895_bib0027","article-title":"Fast unsupervised brain anomaly detection and segmentation with diffusion models","author":"Pinaya","year":"2022","journal-title":"MICCAI"},{"key":"10.1016\/j.media.2025.103895_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102475","article-title":"Unsupervised brain imaging 3d anomaly detection and segmentation with transformers","volume":"79","author":"Pinaya","year":"2022","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.media.2025.103895_bib0029","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.media.2004.06.007","article-title":"A brain tumor segmentation framework based on outlier detection","volume":"8","author":"Prastawa","year":"2004","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.media.2025.103895_bib0030","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1002\/hbm.20906","article-title":"The SRI24 multichannel atlas of normal adult human brain structure","volume":"31","author":"Rohlfing","year":"2010","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.media.2025.103895_bib0031","series-title":"MICCAI Workshop on Deep Generative Models","first-page":"34","article-title":"What is healthy? generative counterfactual diffusion for lesion localization","author":"Sanchez","year":"2022"},{"key":"10.1016\/j.media.2025.103895_bib0032","series-title":"Medical Imaging 2018: Computer-Aided Diagnosis","article-title":"A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes","volume":"10575","author":"Sato","year":"2018"},{"key":"10.1016\/j.media.2025.103895_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.01.010","article-title":"F-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks","volume":"54","author":"Schlegl","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103895_bib0034","series-title":"Information Processing in Medical Imaging","first-page":"146","article-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"Schlegl","year":"2017"},{"issue":"8","key":"10.1016\/j.media.2025.103895_bib0035","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/42.938237","article-title":"Automated segmentation of multiple sclerosis lesions by model outlier detection","volume":"20","author":"Van Leemput","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103895_bib0036","unstructured":"Whitaker, J., 2023. Multi Resolution Noise for Diffusion Model Training. https:\/\/wandb.ai\/johnowhitaker\/multires_noise\/reports\/Multi-Resolution-Noise-for-Diffusion-Model-Training\u2013VmlldzozNjYyOTU2."},{"key":"10.1016\/j.media.2025.103895_bib0037","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","article-title":"AnoDDPM: anomaly detection with denoising diffusion probabilistic models using simplex noise","author":"Wyatt","year":"2022"},{"key":"10.1016\/j.media.2025.103895_bib0038","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3389\/fninf.2019.00046","article-title":"Systematic differences between perceptually relevant image statistics of brain MRI and natural images","volume":"13","author":"Xu","year":"2019","journal-title":"Front. Neuroinform."},{"issue":"3","key":"10.1016\/j.media.2025.103895_bib0039","article-title":"A new criterion for automatic multilevel thresholding","volume":"4","author":"Yen","year":"1995","journal-title":"IEEE Trans. Image Process."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525004414?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525004414?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T19:51:59Z","timestamp":1784231519000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841525004414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":39,"alternative-id":["S1361841525004414"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2025.103895","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Unsupervised anomaly detection in medical imaging using aggregated normative diffusion","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2025.103895","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103895"}}