Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. The article also reviews three main approaches for cell image classification most often used: numerical feature extraction, end-to-end classification with neural networks (NNs), and transport-based morphometry (TBM). In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. The results computed using four publicly available datasets show that numerical features tend to carry the best discriminative information for most of the classification tasks. Results also show that NN-based methods produce state-of-the-art results in the dataset that contains a relatively large number of training samples. Data augmentation or the choice of a more recently reported architecture does not necessarily improve the classification performance of NNs in the datasets with limited number of training samples. If understanding and visualization are desired aspects, TBM methods can offer the ability to invert classification functions, and thus can aid in the interpretation of results. These and other comparison outcomes are discussed with the aim of clarifying the advantages and disadvantages of each method. © 2020 International Society for Advancement of Cytometry.

中文翻译：

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细胞图像分类：比较概述。

细胞图像分类方法目前被用于细胞生物学和医学的众多应用中。应用包括了解基因和药物在筛选实验中的作用，了解不同蛋白质的作用和亚细胞定位，以及根据使用细胞学和组织学技术获得的图像诊断和预后癌症。本文还回顾了最常用的细胞图像分类的三种主要方法：数值特征提取、使用神经网络 (NN) 进行端到端分类和基于传输的形态测量 (TBM)。此外，我们提供了对四种不同细胞成像数据集的比较，以突出每种方法的相对优势。使用四个公开可用的数据集计算的结果表明，对于大多数分类任务，数值特征往往具有最好的判别信息。结果还表明，基于 NN 的方法在包含相对大量训练样本的数据集中产生了最先进的结果。数据增强或选择最近报道的架构不一定能提高训练样本数量有限的数据集中 NN 的分类性能。如果需要理解和可视化，TBM 方法可以提供反转分类函数的能力，从而有助于解释结果。讨论这些和其他比较结果的目的是澄清每种方法的优点和缺点。