UMIAD-EGMF: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion

Abstract Medical imaging technology has advanced rapidly in recent years; however, abnormalities in medical images are often rare and complex, making sample labels difficult to obtain for supervised learning of detection models. Existing unsupervised anomaly detection methods, which are the mainstream approaches, often struggle with issues such as blurred edges and varying scales of abnormal regions. To address these issues, a novel unsupervised method for medical image anomaly detection is proposed: unsupervised medical image anomaly detection based on edge guidance and multi-scale flow fusion (UMIAD-EGMF). This method excavates rich edge information with scale adaptation and progressively identifies discriminative information for anomaly detection. 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%.