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:

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