Anatomy-aware self-supervised learning for anomaly detection in chest radiographs

In this study, we present a self-supervised learning (SSL)-based model that enables anatomical structure-based unsupervised anomaly detection (UAD). The model employs an anatomy-aware pasting (AnatPaste) augmentation tool that uses a threshold-based lung segmentation pretext task to create anomalies...

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Detalles Bibliográficos
Autores principales: Sato, Junya, Suzuki, Yuki, Wataya, Tomohiro, Nishigaki, Daiki, Kita, Kosuke, Yamagata, Kazuki, Tomiyama, Noriyuki, Kido, Shoji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331430/
https://www.ncbi.nlm.nih.gov/pubmed/37434699
http://dx.doi.org/10.1016/j.isci.2023.107086

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