A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning

BACKGROUND: Pathology synopses consist of semi-structured or unstructured text summarizing visual information by observing human tissue. Experts write and interpret these synopses with high domain-specific knowledge to extract tissue semantics and formulate a diagnosis in the context of ancillary te...

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Detalles Bibliográficos
Autores principales: Mu, Youqing, Tizhoosh, Hamid R., Tayebi, Rohollah Moosavi, Ross, Catherine, Sur, Monalisa, Leber, Brian, Campbell, Clinton J. V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053264/
https://www.ncbi.nlm.nih.gov/pubmed/35602188
http://dx.doi.org/10.1038/s43856-021-00008-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053264/
https://www.ncbi.nlm.nih.gov/pubmed/35602188
http://dx.doi.org/10.1038/s43856-021-00008-0

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