Fluorescence microscopy datasets for training deep neural networks
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Abstract
Background
Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal-to-noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample.
Findings
To use deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high-quality datasets that can be used to train and evaluate deep learning methods under development.
Conclusion
The availability of high-quality data is vital for training convolutional neural networks that are used in current machine learning approaches.
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Now published in GigaScience doi: 10.1093/gigascience/giab032
Guy M. Hagen 1UCCS BioFrontiers Center, University of Colorado at Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Guy M. HagenFor correspondence: ghagen@uccs.eduJustin Bendesky 1UCCS BioFrontiers Center, University of Colorado at Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteRosa Machado 1UCCS BioFrontiers Center, University of Colorado at Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918Find this author on Google ScholarFind this author on PubMedSearch for this author on this …
Now published in GigaScience doi: 10.1093/gigascience/giab032
Guy M. Hagen 1UCCS BioFrontiers Center, University of Colorado at Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Guy M. HagenFor correspondence: ghagen@uccs.eduJustin Bendesky 1UCCS BioFrontiers Center, University of Colorado at Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteRosa Machado 1UCCS BioFrontiers Center, University of Colorado at Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteTram-Anh Nguyen 2George Mason University, 4400 University Drive, Fairfax, Virginia, 22030Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteTanmay Kumar 3Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, California, 93407Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteJonathan Ventura 3Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, California, 93407Find this author on Google ScholarFind this author on PubMedSearch for this author on this site
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giab032 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102721 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102722
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