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bioRxiv · 10.1101/2020.03.20.000133

ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy

Chamier, L. v.·Jukkala, J.·Spahn, C.·Lerche, M.·Hernandez-perez, S.·Mattila, P.·Karinou, E.·Holden, S.·Can Solak, A.·Krull, A.·Buchholz, T.-O.·Jug, F.·Royer, L. A.·Heilemann, M.·Laine, R. F.·Jacquemet, G.·Henriques, R.
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Abstract

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.

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Chamier, L. v., Jukkala, J., Spahn, C., Lerche, M., Hernandez-perez, S., Mattila, P., Karinou, E., Holden, S., Can Solak, A., Krull, A., Buchholz, T.-O., Jug, F., Royer, L. A., Heilemann, M., Laine, R. F., Jacquemet, G., Henriques, R.. 2020-03-20. ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy. https://doi.org/10.1101/2020.03.20.000133

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