Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance
Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance
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DOI：
10.1016/j.marmicro.2019.01.005
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发表时间：
2019-03-01
影响因子：
1.9
通讯作者：
Lobaton, E.
中科院分区：
文献类型：
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作者：
Mitra, R.;Marchitto, T. M.;Lobaton, E.
从沉积物样本中挑选有孔虫是一项必不可少但重复且回报低的任务，非常适合自动化操作。制造挑选机器人的第一步是开发一个自动识别系统。我们使用机器学习技术来训练卷积神经网络（CNNs），以识别六种被古海洋学家广泛使用的现存浮游有孔虫，并将这六个物种与其他分类群区分开来。我们采用先前为图像分类而构建和训练的卷积神经网络。有孔虫的训练和识别使用通过发光二极管（LED）环在16个不同照明角度拍摄的反射光显微镜数字图像。即使训练有限，作为精确率和召回率组合的整体机器准确率也优于80%。我们通过让每个人基于图像识别540个样本，将机器性能与人工挑选者（六位专家和五位新手）进行比较。专家达到了与机器相当的精确率，但召回率较差，平均准确率为63%。新手在精确率和召回率上都比专家得分低，整体准确率为53%。机器在六个物种上的表现相当一致，而参与者的得分则与物种密切相关，这与他们过去的经验和专业知识相符。机器对样本方向（脐面与螺旋面视图）也不如人类敏感。这些结果表明，我们的方法可以为最终的自动化机器人挑选系统提供一个通用的“大脑”。
Picking foraminifera from sediment samples is an essential, but repetitive and low-reward task that is well-suited for automation. The first step toward building a picking robot is the development of an automated identification system. We use machine learning techniques to train convolutional neural networks (CNNs) to identify six species of extant planktic foraminifera that are widely used by paleoceanographers, and to distinguish the six species from other taxa. We employ CNNs that were previously built and trained for image classification. Foraminiferal training and identification use reflected light microscope digital images taken at 16 different illumination angles using a light-emitting diode (LED) ring. Overall machine accuracy, as a combination of precision and recall, is better than 80% even with limited training. We compare machine performance to that of human pickers (six experts and five novices) by tasking each with the identification of 540 specimens based on images. Experts achieved comparable precision but poorer recall relative to the machine, with an average accuracy of 63%. Novices scored lower than experts on both precision and recall, for an overall accuracy of 53%. The machine achieved fairly uniform performance across the six species, while participants' scores were strongly species-dependent, commensurate with their past experience and expertise. The machine was also less sensitive to specimen orientation (umbilical versus spiral views) than the humans. These results demonstrate that our approach can provide a versatile 'brain' for an eventual automated robotic picking system.