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Seeing More: A Future of Augmented Microscopy
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-7034-0850
2018 (English)In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 173, no 3, p. 546-548Article in journal (Refereed) Published
Abstract [en]

Microscope images are information rich. In this issue of Cell, Christiansen et al. show that label-free images of cells can be used to predict fluorescent labels representing cell type, state, and organelle distribution using a deep-learning framework. This paves the way for computationally multiplexed assays derived from inexpensive label-free microscopy. Microscope images are information rich. In this issue of Cell, Christiansen et al. show that label-free images of cells can be used to predict fluorescent labels representing cell type, state, and organelle distribution using a deep-learning framework. This paves the way for computationally multiplexed assays derived from inexpensive label-free microscopy.

Place, publisher, year, edition, pages
Cell Press , 2018. Vol. 173, no 3, p. 546-548
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-227606DOI: 10.1016/j.cell.2018.04.003ISI: 000430677400006PubMedID: 29677507Scopus ID: 2-s2.0-85045300908OAI: oai:DiVA.org:kth-227606DiVA, id: diva2:1204957
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180509

Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-05-14Bibliographically approved

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Lundberg, Emma

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