Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning

BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottlen...

Descripción completa

Detalles Bibliográficos
Autores principales: D’Anniballe, Vincent M., Tushar, Fakrul Islam, Faryna, Khrystyna, Han, Songyue, Mazurowski, Maciej A., Rubin, Geoffrey D., Lo, Joseph Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9011942/
https://www.ncbi.nlm.nih.gov/pubmed/35428335
http://dx.doi.org/10.1186/s12911-022-01843-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9011942/
https://www.ncbi.nlm.nih.gov/pubmed/35428335
http://dx.doi.org/10.1186/s12911-022-01843-4

Ejemplares similares