Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images

Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local pheno...

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Detalles Bibliográficos
Autores principales: Jiménez-Sánchez, Daniel, López-Janeiro, Álvaro, Villalba-Esparza, María, Ariz, Mikel, Kadioglu, Ece, Masetto, Ivan, Goubert, Virginie, Lozano, Maria D., Melero, Ignacio, Hardisson, David, Ortiz-de-Solórzano, Carlos, de Andrea, Carlos E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036616/
https://www.ncbi.nlm.nih.gov/pubmed/36959234
http://dx.doi.org/10.1038/s41746-023-00795-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036616/
https://www.ncbi.nlm.nih.gov/pubmed/36959234
http://dx.doi.org/10.1038/s41746-023-00795-x

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