Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs

Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately label...

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Detalles Bibliográficos
Autores principales: Büttner, Martha, Schneider, Lisa, Krasowski, Aleksander, Krois, Joachim, Feldberg, Ben, Schwendicke, Falk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179289/
https://www.ncbi.nlm.nih.gov/pubmed/37176499
http://dx.doi.org/10.3390/jcm12093058

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10179289/
https://www.ncbi.nlm.nih.gov/pubmed/37176499
http://dx.doi.org/10.3390/jcm12093058

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