Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
NSF Center for Cellular Construction, University of California, San Francisco, San Francisco, CA, USA.
Nat Methods. 2018 Aug;15(8):587-590. doi: 10.1038/s41592-018-0069-0. Epub 2018 Jul 31.
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.
我们介绍了 Quanti.us,这是一个基于众包的图像标注平台,为困难的图像分析问题提供了一种比计算算法更准确的替代方案。我们使用 Quanti.us 完成了各种高通量的图像分析任务,与单个专家注释员完成相同任务所需的分析时间相比,节省了 10-50 倍。我们展示了 Quanti.us 衍生注释和专家衍生注释的等效深度学习性能,这应该允许与定制机器学习算法进行可扩展的集成。