Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Johanna Jukkala"'
Autor:
Lucas von Chamier, Romain F. Laine, Johanna Jukkala, Christoph Spahn, Daniel Krentzel, Elias Nehme, Martina Lerche, Sara Hernández-Pérez, Pieta K. Mattila, Eleni Karinou, Séamus Holden, Ahmet Can Solak, Alexander Krull, Tim-Oliver Buchholz, Martin L. Jones, Loïc A. Royer, Christophe Leterrier, Yoav Shechtman, Florian Jug, Mike Heilemann, Guillaume Jacquemet, Ricardo Henriques
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCos
Externí odkaz:
https://doaj.org/article/71c9afaf6b9849e787693c838589c312
Autor:
Pieta K. Mattila, Lucas von Chamier, Yoav Shechtman, Elias Nehme, Guillaume Jacquemet, Johanna Jukkala, Alexander Krull, Daniel Krentzel, Loic Royer, Tim-Oliver Buchholz, Mike Heilemann, Christophe Leterrier, Martina Lerche, Romain F. Laine, Ricardo Henriques, Florian Jug, Sara Hernández-Pérez, Martin L. Jones, Eleni Karinou, Ahmet Can Solak, Christoph Spahn, Seamus Holden
Publikováno v:
Nature Communications
Nature Communications, Nature Publishing Group, 2021, 12, ⟨10.1038/s41467-021-22518-0⟩
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
Nature Communications, 2021, 12, ⟨10.1038/s41467-021-22518-0⟩
Nature Communications, Nature Publishing Group, 2021, 12, ⟨10.1038/s41467-021-22518-0⟩
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
Nature Communications, 2021, 12, ⟨10.1038/s41467-021-22518-0⟩
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::020f134a7f24e39c4432fd2895e9d76b
https://hal-amu.archives-ouvertes.fr/hal-03233449/document
https://hal-amu.archives-ouvertes.fr/hal-03233449/document
Autor:
Elias Nehme, Martina Lerche, Guillaume Jacquemet, Mike Heilemann, Martin L. Jones, Eleni Karinou, Sara Hernández-Pérez, Alexander Krull, Yoav Shechtman, Chamier Lv, Ricardo Henriques, Florian Jug, Tim-Oliver Buchholz, Daniel Krentzel, Loic Royer, Christophe Leterrier, Christoph Spahn, Pieta K. Mattila, Johanna Jukkala, Seamus Holden, Romain F. Laine, Ahmet Can Solak
The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60d0b376059d439c3d86fbf2e943d6e8
https://doi.org/10.1101/2020.03.20.000133
https://doi.org/10.1101/2020.03.20.000133