Tissue contamination challenges the credibility of machine learning models in real world digital pathology

Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue....

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Detalles Bibliográficos
Autores principales: Irmakci, Ismail, Nateghi, Ramin, Zhou, Rujoi, Ross, Ashley E., Yang, Ximing J., Cooper, Lee A. D., Goldstein, Jeffery A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187357/
https://www.ncbi.nlm.nih.gov/pubmed/37205404
http://dx.doi.org/10.1101/2023.04.28.23289287

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187357/
https://www.ncbi.nlm.nih.gov/pubmed/37205404
http://dx.doi.org/10.1101/2023.04.28.23289287

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