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

被引：38

作者：

Hughes, Alex J. ^{[1
,2
,4
]}

Mornin, Joseph D.

Biswas, Sujoy K. ^{[2
,3
]}

Beck, Lauren E. ^{[4
]}

Bauer, David P. ^{[2
,5
]}

Raj, Arjun ^{[4
]}

Bianco, Simone ^{[2
]}

Gartner, Zev J. ^{[1
,2
,6
]}

机构：

[1] Univ Calif San Francisco, Dept Pharmaceut Chem, San Francisco, CA 94143 USA

[2] Univ Calif San Francisco, NSF Ctr Cellular Construct, San Francisco, CA 94143 USA

[3] IBM Almaden Res Ctr, Dept Ind & Appl Genom, IBM Accelerated Discovery Lab, San Jose, CA USA

[4] Univ Penn, Dept Bioengn, Philadelphia, PA 19104 USA

[5] Univ Calif San Francisco, Dept Cellular & Mol Pharmacol, San Francisco, CA 94143 USA

[6] Chan Zuckerberg Biohub, San Francisco, CA 94158 USA

来源：

NATURE METHODS | 2018年 / 15卷 / 08期

关键词：

D O I：

10.1038/s41592-018-0069-0

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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 10-50x 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.

引用

页码：587 / +

页数：8

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