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 mainstre...

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Номзүйн дэлгэрэнгүй

Үндсэн зохиолчид:	Zhirong Li, Guangfeng Lin, Dou Zhang, Rongxin Huang, Jing Yang
Формат:	Artigo
Хэл сонгох:	Inglês
Хэвлэсэн:	SpringerOpen 2026-03-01
Цуврал:	Visual Computing for Industry, Biomedicine, and Art
Нөхцлүүд:	Medical image analysis Anomaly detection Unsupervised learning Edge guidance Multi-scale flow fusion
Онлайн хандалт:	https://doi.org/10.1186/s42492-026-00215-3
Шошгууд:	Шошго нэмэх Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!