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A deep learning framework for nucleus segmentation using image style transfer
Reka Hollandi, Abel Szkalisity, Timea Toth, Ervin Tasnadi, Csaba Molnar, Botond Mathe, Istvan Grexa, Jozsef Molnar, Arpad Balind, Mate Gorbe, Maria Kovacs, Ede Migh, Allen Goodman, Tamas Balassa, Krisztian Koos, Wenyu Wang, Norbert Bara, Ferenc Kovacs, Lassi Paavolainen, Tivadar Danka, Andras Kriston, Anne E. Carpenter, Kevin Smith, Peter Horvath
doi: https://doi.org/10.1101/580605
Reka Hollandi
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Abel Szkalisity
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Timea Toth
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Ervin Tasnadi
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Csaba Molnar
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Botond Mathe
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Istvan Grexa
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Jozsef Molnar
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Arpad Balind
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Mate Gorbe
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Maria Kovacs
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Ede Migh
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Allen Goodman
2Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
Tamas Balassa
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Krisztian Koos
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Wenyu Wang
3Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Norbert Bara
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
4Single-Cell Technologies Ltd, Szeged, Hungary.
Ferenc Kovacs
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
4Single-Cell Technologies Ltd, Szeged, Hungary.
Lassi Paavolainen
3Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Tivadar Danka
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
Andras Kriston
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
4Single-Cell Technologies Ltd, Szeged, Hungary.
Anne E. Carpenter
2Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
Kevin Smith
5School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.
6Science for Life Laboratory, Solna, Sweden.
Peter Horvath
1Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary.
3Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Posted March 17, 2019.
A deep learning framework for nucleus segmentation using image style transfer
Reka Hollandi, Abel Szkalisity, Timea Toth, Ervin Tasnadi, Csaba Molnar, Botond Mathe, Istvan Grexa, Jozsef Molnar, Arpad Balind, Mate Gorbe, Maria Kovacs, Ede Migh, Allen Goodman, Tamas Balassa, Krisztian Koos, Wenyu Wang, Norbert Bara, Ferenc Kovacs, Lassi Paavolainen, Tivadar Danka, Andras Kriston, Anne E. Carpenter, Kevin Smith, Peter Horvath
bioRxiv 580605; doi: https://doi.org/10.1101/580605
A deep learning framework for nucleus segmentation using image style transfer
Reka Hollandi, Abel Szkalisity, Timea Toth, Ervin Tasnadi, Csaba Molnar, Botond Mathe, Istvan Grexa, Jozsef Molnar, Arpad Balind, Mate Gorbe, Maria Kovacs, Ede Migh, Allen Goodman, Tamas Balassa, Krisztian Koos, Wenyu Wang, Norbert Bara, Ferenc Kovacs, Lassi Paavolainen, Tivadar Danka, Andras Kriston, Anne E. Carpenter, Kevin Smith, Peter Horvath
bioRxiv 580605; doi: https://doi.org/10.1101/580605
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