RT Journal Article
SR Electronic
T1 ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.20.000133
DO 10.1101/2020.03.20.000133
A1 Lucas von Chamier
A1 Romain F. Laine
A1 Johanna Jukkala
A1 Christoph Spahn
A1 Daniel Krentzel
A1 Elias Nehme
A1 Martina Lerche
A1 Sara Hernández-Pérez
A1 Pieta K. Mattila
A1 Eleni Karinou
A1 Séamus Holden
A1 Ahmet Can Solak
A1 Alexander Krull
A1 Tim-Oliver Buchholz
A1 Martin L. Jones
A1 Loïc A Royer
A1 Christophe Leterrier
A1 Yoav Shechtman
A1 Florian Jug
A1 Mike Heilemann
A1 Guillaume Jacquemet
A1 Ricardo Henriques
YR 2020
UL http://biorxiv.org/content/early/2020/08/17/2020.03.20.000133.abstract
AB 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, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation. We demonstrate the application of the platform to study multiple biological processes.Competing Interest StatementThe authors have declared no competing interest.