BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning...

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Vydané v:	bioRxiv
Hlavní autori:	Ouyang, Wei, Beuttenmueller, Fynn, Gómez-de-Mariscal, Estibaliz, Pape, Constantin, Burke, Tom, Garcia-López-de-Haro, Carlos, Russell, Craig, Moya-Sans, Lucía, de-la-Torre-Gutiérrez, Cristina, Schmidt, Deborah, Kutra, Dominik, Novikov, Maksim, Weigert, Martin, Schmidt, Uwe, Bankhead, Peter, Jacquemet, Guillaume, Sage, Daniel, Henriques, Ricardo, Muñoz-Barrutia, Arrate, Lundberg, Emma, Jug, Florian, Kreshuk, Anna
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Cold Spring Harbor Laboratory 08.06.2022
Vydanie:	1.1
Predmet:	Bioinformatics
ISSN:	2692-8205
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