机构:
KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, S-17121 Stockholm, SwedenKTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, S-17121 Stockholm, Sweden
Sullivan, Devin P.
[1
]
Lundberg, Emma
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KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, S-17121 Stockholm, SwedenKTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, S-17121 Stockholm, Sweden
Lundberg, Emma
[1
]
机构:
[1] KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Sci Life Lab, S-17121 Stockholm, Sweden
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.