Quality assurance for automatically generated contours with additional deep learning

OBJECTIVE: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not...

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Detalles Bibliográficos
Autores principales: Isaksson, Lars Johannes, Summers, Paul, Bhalerao, Abhir, Gandini, Sara, Raimondi, Sara, Pepa, Matteo, Zaffaroni, Mattia, Corrao, Giulia, Mazzola, Giovanni Carlo, Rotondi, Marco, Lo Presti, Giuliana, Haron, Zaharudin, Alessi, Sara, Pricolo, Paola, Mistretta, Francesco Alessandro, Luzzago, Stefano, Cattani, Federica, Musi, Gennaro, De Cobelli, Ottavio, Cremonesi, Marta, Orecchia, Roberto, Marvaso, Giulia, Petralia, Giuseppe, Jereczek-Fossa, Barbara Alicja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385913/
https://www.ncbi.nlm.nih.gov/pubmed/35976491
http://dx.doi.org/10.1186/s13244-022-01276-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385913/
https://www.ncbi.nlm.nih.gov/pubmed/35976491
http://dx.doi.org/10.1186/s13244-022-01276-7

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