Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we...

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Detalles Bibliográficos
Autores principales: MacDonald, Samual, Foley, Helena, Yap, Melvyn, Johnston, Rebecca L., Steven, Kaiah, Koufariotis, Lambros T., Sharma, Sowmya, Wood, Scott, Addala, Venkateswar, Pearson, John V., Roosta, Fred, Waddell, Nicola, Kondrashova, Olga, Trzaskowski, Maciej
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/PMC10164181/
https://www.ncbi.nlm.nih.gov/pubmed/37149669
http://dx.doi.org/10.1038/s41598-023-31126-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164181/
https://www.ncbi.nlm.nih.gov/pubmed/37149669
http://dx.doi.org/10.1038/s41598-023-31126-5

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