Pumping up the volume

The time and cost of annotating ground-truth images and network training are major challenges to utilizing machine learning to automate the mining of volume electron microscopy data. In this issue, Gallusser et al. (2023. J. Cell Biol. https://doi.org/10.1083/jcb.202208005) present a less computatio...

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Detalles Bibliográficos
Autor principal: Galbraith, Catherine G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Rockefeller University Press 2023
Materias:
Spotlight
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930139/
https://www.ncbi.nlm.nih.gov/pubmed/36696087
http://dx.doi.org/10.1083/jcb.202212042
Descripción
Sumario:The time and cost of annotating ground-truth images and network training are major challenges to utilizing machine learning to automate the mining of volume electron microscopy data. In this issue, Gallusser et al. (2023. J. Cell Biol. https://doi.org/10.1083/jcb.202208005) present a less computationally intense pipeline to detect a single type of organelle using a limited number of loosely annotated images.

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