Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with...

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Detalles Bibliográficos
Autores principales: Marini, Niccolò, Marchesin, Stefano, Otálora, Sebastian, Wodzinski, Marek, Caputo, Alessandro, van Rijthoven, Mart, Aswolinskiy, Witali, Bokhorst, John-Melle, Podareanu, Damian, Petters, Edyta, Boytcheva, Svetla, Buttafuoco, Genziana, Vatrano, Simona, Fraggetta, Filippo, van der Laak, Jeroen, Agosti, Maristella, Ciompi, Francesco, Silvello, Gianmaria, Muller, Henning, Atzori, Manfredo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307641/
https://www.ncbi.nlm.nih.gov/pubmed/35869179
http://dx.doi.org/10.1038/s41746-022-00635-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307641/
https://www.ncbi.nlm.nih.gov/pubmed/35869179
http://dx.doi.org/10.1038/s41746-022-00635-4

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