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Name: ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset (see our Wiki for details) Data type: Microscopy images (fluorescence) Microscopy data type: Fluorescence microscopy (paxillin-GFP) Microscope: Spinning disk confocal microscope with a 63x 1.4 NA objective Cell type: U-251 glioma cells, endogenously expressing paxillin-GFP File format: .tif (16-bit) Image size: 512x512 (Pixel size: 248 nm) Author(s): Aki Stubb1, Guillaume Jacquemet1,2 and Johanna Ivaska1 Contact email: guillaume.jacquemet@abo.fi Affiliation: 1) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland 2) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland Associated publication: Stubb et al. 2020, Nano letters DOI: 10.1021/acs.nanolett.9b04083 Funding bodies: A.S. has been supported by the University of Turku Doctoral programme for Molecular Medicine (TuDMM).
Microscopy, Denoising, Noise2Void, Deep learning
Microscopy, Denoising, Noise2Void, Deep learning
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