Deep active learning for classifying cancer pathology reports

BACKGROUND: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount o...

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Detalles Bibliográficos
Autores principales: De Angeli, Kevin, Gao, Shang, Alawad, Mohammed, Yoon, Hong-Jun, Schaefferkoetter, Noah, Wu, Xiao-Cheng, Durbin, Eric B., Doherty, Jennifer, Stroup, Antoinette, Coyle, Linda, Penberthy, Lynne, Tourassi, Georgia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941989/
https://www.ncbi.nlm.nih.gov/pubmed/33750288
http://dx.doi.org/10.1186/s12859-021-04047-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941989/
https://www.ncbi.nlm.nih.gov/pubmed/33750288
http://dx.doi.org/10.1186/s12859-021-04047-1

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