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.

中文翻译：

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Quanti.us：快速、灵活、基于人群的图像注释工具

我们描述了 Quanti.us，一个基于人群的图像注释平台，它为解决困难的图像分析问题提供了计算算法的准确替代方案。我们使用 Quanti.us 执行各种中等吞吐量的图像分析任务，与单个专家注释者完成相同任务所需的分析时间相比，节省了 10-50 倍的分析时间。我们展示了 Quanti.us 派生的注释和专家派生的注释的同等深度学习性能，这应该允许与定制的机器学习算法进行可扩展的集成。