Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm...

Descripción completa

Detalles Bibliográficos
Autores principales: Saldanha, Oliver Lester, Quirke, Philip, West, Nicholas P., James, Jacqueline A., Loughrey, Maurice B., Grabsch, Heike I., Salto-Tellez, Manuel, Alwers, Elizabeth, Cifci, Didem, Ghaffari Laleh, Narmin, Seibel, Tobias, Gray, Richard, Hutchins, Gordon G. A., Brenner, Hermann, van Treeck, Marko, Yuan, Tanwei, Brinker, Titus J., Chang-Claude, Jenny, Khader, Firas, Schuppert, Andreas, Luedde, Tom, Trautwein, Christian, Muti, Hannah Sophie, Foersch, Sebastian, Hoffmeister, Michael, Truhn, Daniel, Kather, Jakob Nikolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205774/
https://www.ncbi.nlm.nih.gov/pubmed/35469069
http://dx.doi.org/10.1038/s41591-022-01768-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205774/
https://www.ncbi.nlm.nih.gov/pubmed/35469069
http://dx.doi.org/10.1038/s41591-022-01768-5

Ejemplares similares