We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

中文翻译：

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定量相图像中脂质液滴的深度学习分类

我们报告了无标记的定量相图像中监督的机器学习在脂滴自动分类中的应用。通过比较生物医学成像和遥感中常用的各种机器学习方法，我们发现卷积神经网络在数量和质量上均优于其他方法。我们描述了我们的成像方法，所有已实施的机器学习方法，以及它们在计算效率，所需的培训资源以及跨多个指标测得的相对方法性能方面的性能。总体而言，我们的结果表明，定量相成像与机器学习相结合，可以在单个活细胞中进行准确的脂质滴分类。因此，