Empowering digital pathology applications through explainable knowledge extraction tools

Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction sys...

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Detalles Bibliográficos
Autores principales: Marchesin, Stefano, Giachelle, Fabio, Marini, Niccolò, Atzori, Manfredo, Boytcheva, Svetla, Buttafuoco, Genziana, Ciompi, Francesco, Di Nunzio, Giorgio Maria, Fraggetta, Filippo, Irrera, Ornella, Müller, Henning, Primov, Todor, Vatrano, Simona, Silvello, Gianmaria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577130/
https://www.ncbi.nlm.nih.gov/pubmed/36268087
http://dx.doi.org/10.1016/j.jpi.2022.100139

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577130/
https://www.ncbi.nlm.nih.gov/pubmed/36268087
http://dx.doi.org/10.1016/j.jpi.2022.100139

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