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Nat Methods. 2018 Aug;15(8):587-590. doi: 10.1038/s41592-018-0069-0. Epub 2018 Jul 31.

Quanti.us: a tool for rapid, flexible, crowd-based annotation of images.

Author information

1
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
2
NSF Center for Cellular Construction, University of California, San Francisco, San Francisco, CA, USA.
3
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
4
Independent Researcher, Berkeley, CA, USA.
5
Department of Industrial and Applied Genomics, IBM Accelerated Discovery Laboratory, IBM Almaden Research Center, San Jose, CA, USA.
6
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
7
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA. zev.gartner@ucsf.edu.
8
NSF Center for Cellular Construction, University of California, San Francisco, San Francisco, CA, USA. zev.gartner@ucsf.edu.
9
Chan Zuckerberg Biohub, San Francisco, CA, USA. zev.gartner@ucsf.edu.

Abstract

We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

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PMID:
30065368
DOI:
10.1038/s41592-018-0069-0
[Indexed for MEDLINE]

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