Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
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DOI：
10.1016/j.btre.2019.e00321
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发表时间：
2019-06-01
影响因子：
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通讯作者：
Dopson, Mark
中科院分区：
文献类型：
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作者：
Buetti-Dinh, Antoine;Galli, Vanni;Dopson, Mark
背景：深度神经网络已成功应用于计算机视觉的各个领域。然而，它们仅在少数情况下优于人类能力。方法：在硫化物矿物上形成的生物膜定殖模式上测试了深度神经网络与人类专家对显微镜图像进行分类的能力，所述硫化物矿物由附着于黄铜矿样品颗粒的多达三种不同的生物浸出细菌物种组成。结果：每个类别的显微镜图像数量较少（
Background: Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases.Methods: The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles.Results: A low number of microscopy images per category (