Unbiased single-cell morphology with self-supervised vision transformers

Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transforme...

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Detalles Bibliográficos
Autores principales: Doron, Michael, Moutakanni, Théo, Chen, Zitong S., Moshkov, Nikita, Caron, Mathilde, Touvron, Hugo, Bojanowski, Piotr, Pernice, Wolfgang M., Caicedo, Juan C.
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/PMC10312751/
https://www.ncbi.nlm.nih.gov/pubmed/37398158
http://dx.doi.org/10.1101/2023.06.16.545359

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312751/
https://www.ncbi.nlm.nih.gov/pubmed/37398158
http://dx.doi.org/10.1101/2023.06.16.545359

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