Can Machine Learning Be Better than Biased Readers?

Background: Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then combine the labeled data for training a ML model....

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Detalles Bibliográficos
Autores principales: Hibi, Atsuhiro, Zhu, Rui, Tyrrell, Pascal N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204355/
https://www.ncbi.nlm.nih.gov/pubmed/37218934
http://dx.doi.org/10.3390/tomography9030074

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204355/
https://www.ncbi.nlm.nih.gov/pubmed/37218934
http://dx.doi.org/10.3390/tomography9030074

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