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UMIAD-EGMF captures contextual information around anomaly boundaries via low-level feature fusion (enhancing boundary details with the edge guidance module; EGM), integrates EGM-extracted edge information into deeper features using the edge aggregation module, and merges multi-scale feature maps to capture common anomaly features (subtle and significant) through multi-scale flow fusion. Experiments on breast ultrasound images (BUSI), brain magnetic resonance imaging (brain MRI), and head computed tomography (head CT) datasets demonstrate that UMIAD-EGMF outperforms the state-of-the-art methods. Specifically, on the BUSI dataset, the segmentation area under the precision-recall curve for object localization (AUPRO) of UMIAD-EGMF reaches 63.36%, surpassing that of the multi-scale low-level feature enhancement U-Net (MLFEU-net) by 0.01%; on the brain MRI dataset, its segmentation AUPRO is 90.83%, outperforming that of MLFEU-net by 0.33%; and on the head CT dataset, its segmentation AUPRO is 62.24%, exceeding that of MedMAE by 2.37%.<\/jats:p>","DOI":"10.1186\/s42492-026-00215-3","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T23:03:22Z","timestamp":1772406202000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UMIAD-EGMF: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion"],"prefix":"10.1186","volume":"9","author":[{"given":"Zhirong","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6191-1102","authenticated-orcid":false,"given":"Guangfeng","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Dou","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Rongxin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"215_CR1","doi-asserted-by":"publisher","first-page":"108846","DOI":"10.1016\/j.knosys.2022.108846","volume":"248","author":"YK Cao","year":"2022","unstructured":"Cao YK, Wan Q, Shen WM, Gao L (2022) Informative knowledge distillation for image anomaly segmentation. 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As such, no ethical approval was required from an ethics committee. All data used in this study were obtained from publicly accessible sources that comply with ethical standards for data sharing. The study adheres to the ethical principles of scientific research, ensuring the use of publicly available data without any infringement on privacy or consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"5"}}