Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging
Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging
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DOI：
10.3389/fninf.2019.00029
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发表时间：
2019-05-08
影响因子：
3.5
通讯作者：
Rokem, Ariel
中科院分区：
文献类型：
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作者：
Keshavan, Anisha;Yeatman, Jason D.;Rokem, Ariel
大数据承诺通过数据驱动的发现来推进科学。然而，许多标准实验室协议依赖于手动检查，这对于大规模数据集是不可行的。同时，自动化方法缺乏专家检查的准确性。我们建议（1）从专业标记的数据开始，（2）通过吸引公民科学家的Web应用程序来放大标签，（3）在放大的标签上训练机器学习，以模仿专家。为了证明这一点，我们开发了一个系统来质量控制大脑磁共振图像。公民科学家通过一个简单的网络界面放大了专家标记的数据。然后，基于公民科学家标签，训练深度学习算法来预测数据质量。深度学习的表现以及用于质量控制的专门算法（AUC = 0.99）。将公民科学和深度学习相结合，可以概括和扩展专家决策;这在专业化、自动化工具尚不存在的学科中尤为重要。
Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination. We propose to (1) start with expertly labeled data, (2) amplify labels through web applications that engage citizen scientists, and (3) train machine learning on amplified labels, to emulate the experts. Demonstrating this, we developed a system to quality control brain magnetic resonance images. Expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99). Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist.