{"entity": "publication", "iuid": "81c8b112179c499e9a80b807834158be", "timestamp": "2026-08-29T04:37:05.890Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/81c8b112179c499e9a80b807834158be.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/81c8b112179c499e9a80b807834158be"}}, "title": "Seeing More: A Future of Augmented Microscopy.", "authors": [{"family": "Sullivan", "given": "Devin P", "initials": "DP"}, {"family": "Lundberg", "given": "Emma", "initials": "E"}], "type": "journal article", "published": "2018-04-19", "journal": {"title": "Cell", "issn": "1097-4172", "volume": "173", "issue": "3", "pages": "546-548", "issn-l": "0092-8674"}, "abstract": "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.", "doi": "10.1016/j.cell.2018.04.003", "pmid": "29677507", "labels": [], "xrefs": [{"db": "pii", "key": "S0092-8674(18)30456-2"}], "notes": [], "created": "2026-08-20T06:50:09.174Z", "modified": "2026-08-20T06:50:09.219Z"}