Automated screening of computed tomography using weakly supervised anomaly detection

BACKGROUND: Current artificial intelligence studies for supporting CT screening tasks depend on either supervised learning or detecting anomalies. However, the former involves a heavy annotation workload owing to requiring many slice-wise annotations (ground truth labels); the latter is promising, b...

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Detalles Bibliográficos
Autores principales: Hibi, Atsuhiro, Cusimano, Michael D., Bilbily, Alexander, Krishnan, Rahul G., Tyrrell, Pascal N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226438/
https://www.ncbi.nlm.nih.gov/pubmed/37247113
http://dx.doi.org/10.1007/s11548-023-02965-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226438/
https://www.ncbi.nlm.nih.gov/pubmed/37247113
http://dx.doi.org/10.1007/s11548-023-02965-4

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