AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows

INTRODUCTION: Methods that automatically flag poor performing predictions are drastically needed to safely implement machine learning workflows into clinical practice as well as to identify difficult cases during model training. METHODS: Disagreement between the fivefold cross-validation sub-models...

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Detalles Bibliográficos
Autores principales: Gottlich, Harrison C., Korfiatis, Panagiotis, Gregory, Adriana V., Kline, Timothy L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Radiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540615/
https://www.ncbi.nlm.nih.gov/pubmed/37780641
http://dx.doi.org/10.3389/fradi.2023.1223294

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540615/
https://www.ncbi.nlm.nih.gov/pubmed/37780641
http://dx.doi.org/10.3389/fradi.2023.1223294

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