DeepBacs

DeepBacs demonstrates the potential of open-source deep-learning approaches in microbiological research. We provide dataset that can be used to train models for different tasks, e.g. image segmentation, denoising, artificial labeling or prediction of super-resolution images.

🔗Source-Code Repository

Main publication:

Other publications using resource:

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches *Christoph Spahn*, *Estibaliz Gómez-de-Mariscal*, *Romain F Laine*, *Pedro M Pereira*, *Lucas von Chamier*, Mia Conduit, Mariana G Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, *Ricardo Henriques* Published in Nature Communications Biology, July 2022 (see publication) Research themes: New Methods, Software Type: Paper, Corresponding author

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis Wei Ouyang, Fynn Beuttenmueller, *Estibaliz Gómez-de-Mariscal*, Constantin Pape, Tom Burke, Carlos Garcia-López-de-Haro, Craig Russell, Lucía Moya-Sans, Cristina de-la-Torre-Gutiérrez, Deborah Schmidt, Dominik Kutra, Maksim Novikov, Martin Weigert, Uwe Schmidt, Peter Bankhead, Guillaume Jacquemet, Daniel Sage, *Ricardo Henriques*, Arrate Muñoz-Barrutia, Emma Lundberg, Florian Jug, Anna Kreshuk Published in bioRxiv, June 2022 (see preprint) Research themes: New Methods, Software Type: Preprint