Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach

Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach

Abstract Background Histopathological classification of Wilms tumors determines treatment regimen. Machine learning has been shown to contribute to histopathological classification in various malignancies but requires large numbers of manually annotated images and thus specific pathological knowledg...

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Bibliografische gegevens
Hoofdauteurs: Jikke J. Rutgers, Tessa Bánki, Ananda van der Kamp, Tomas J. Waterlander, Marijn A. Scheijde-Vermeulen, Marry M. van den Heuvel-Eibrink, Jeroen A. W. M. van der Laak, Marta Fiocco, Annelies M. C. Mavinkurve-Groothuis, Ronald R. de Krijger
Formaat: Artikel
Taal:English
Gepubliceerd in: BMC 2021-08-01
Reeks:Diagnostic Pathology
Onderwerpen:
Wilms tumor
Interobserver variability
Machine learning
Histopathology
Classification
AI (artificial intelligence)
Online toegang:https://doi.org/10.1186/s13000-021-01136-w

Internet

https://doi.org/10.1186/s13000-021-01136-w

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