JDLL: a library to run deep learning models on Java bioimage informatics platforms
Nat Methods
.
2024 Jan;21(1):7-8.
doi: 10.1038/s41592-023-02129-x.
Authors
Carlos García López de Haro
1
,
Stéphane Dallongeville
1
2
,
Thomas Musset
1
2
,
Estibaliz Gómez-de-Mariscal
3
,
Daniel Sage
4
,
Wei Ouyang
5
,
Arrate Muñoz-Barrutia
6
,
Jean-Yves Tinevez
7
,
Jean-Christophe Olivo-Marin
8
9
Affiliations
1
Bioimage Analysis Unit, Institut Pasteur, Université Paris Cité, Paris, France.
2
CNRS UMR 3691, Institut Pasteur, Paris, France.
3
Instituto Gulbenkian de Ciência, Lisbon, Portugal.
4
Biomedical Imaging Group and Center for Imaging, Ecole Polytechnique de Lausanne (EPFL), Lausanne, Switzerland.
5
Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
6
Biomedical Sciences and Engineering Laboratory, Universidad Carlos III de Madrid, Leganés, Spain.
7
Image Analysis Hub, Institut Pasteur, Université Paris Cité, Paris, France.
[email protected]
.
8
Bioimage Analysis Unit, Institut Pasteur, Université Paris Cité, Paris, France.
[email protected]
.
9
CNRS UMR 3691, Institut Pasteur, Paris, France.
[email protected]
.
PMID:
38191929
DOI:
10.1038/s41592-023-02129-x
No abstract available
Publication types
Letter
MeSH terms
Deep Learning*
Gene Library
Indonesia
Grants and funding
ANR-10-INBS-04/Agence Nationale de la Recherche (French National Research Agency)
FET-OPEN-2018-2019-2020-01 862840/EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Future and Emerging Technologies (H2020 Excellent Science - Future and Emerging Technologies)
PID2019-109820RB-I00/Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación)