Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries

BACKGROUND: While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product...

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Detalles Bibliográficos
Autores principales: Tillmanns, Niklas, Lum, Avery E, Cassinelli, Gabriel, Merkaj, Sara, Verma, Tej, Zeevi, Tal, Staib, Lawrence, Subramanian, Harry, Bahar, Ryan C, Brim, Waverly, Lost, Jan, Jekel, Leon, Brackett, Alexandria, Payabvash, Sam, Ikuta, Ichiro, Lin, MingDe, Bousabarah, Khaled, Johnson, Michele H, Cui, Jin, Malhotra, Ajay, Omuro, Antonio, Turowski, Bernd, Aboian, Mariam S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446682/
https://www.ncbi.nlm.nih.gov/pubmed/36071926
http://dx.doi.org/10.1093/noajnl/vdac093

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446682/
https://www.ncbi.nlm.nih.gov/pubmed/36071926
http://dx.doi.org/10.1093/noajnl/vdac093

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