A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning–based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.

细胞的表型是几个细胞过程通过复杂的分子相互作用网络的顶点，最终导致独特的形态特征。视觉细胞表型是图像中这些可观察到的细胞特征的表征和量化。最近，细胞表型在规模、分辨率和通量方面经历了巨大的变革，这归功于用于成像细胞的电子、光学和化学技术的进步。再加上基于深度学习的计算工具的快速发展，这些进步为各种高通量细胞生物学应用的创新开辟了新途径。在这里，我们回顾了深度学习为识别、分析、并预测视觉表型以回答重要的生物学问题。随着成像分析的复杂性和规模的增加，深度学习提供了计算解决方案来阐明以前未探索的细胞表型的细节。