Publication
Nature Methods
Paper

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

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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.

Date

31 Jul 2018

Publication

Nature Methods

Authors

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