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Computer-aided imaging

Deep learning to predict microscope images

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A type of neural network first described in 2015 can be trained to translate between images of the same field of view acquired by different modalities. Trained networks can use information inherent in grayscale images of cells to predict fluorescent signals.

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Fig. 1: Translation, classification, and prediction.

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Acknowledgements

We are grateful to J. Markoff, J. Yosinski, J. Clune, G. Johnson, M. Maleckar, W. Peria, and other colleagues for useful communications and insight, and for support from R21 CA223901 to R.B. and U54 CA132831 (NMSU/FHCRC Partnership for the Advancement of Cancer Research) to R.B. and L.B.

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Authors and Affiliations

  1. Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Roger Brent

  2. Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, NM, USA

    Laura Boucheron

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  1. Roger Brent
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  2. Laura Boucheron
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Correspondence to Roger Brent.

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Brent, R., Boucheron, L. Deep learning to predict microscope images. Nat Methods 15, 868–870 (2018). https://doi.org/10.1038/s41592-018-0194-9

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