In this review, we focus on the applications of machine learning methods for analyzing image data acquired in imaging flow cytometry technologies. We propose that the analysis approaches can be categorized into two groups based on the type of data, raw imaging signals or features explicitly extracted from images, being analyzed by a trained model. We hope that this categorization is helpful for understanding uniqueness, differences and opportunities when the machine learning-based analysis is implemented in recently developed 'imaging' cell sorters.

中文翻译：

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实施用于流式细胞术成像的机器学习方法

在这篇综述中，我们专注于机器学习方法在分析成像流式细胞术技术中获得的图像数据的应用。我们建议可以根据数据类型、原始成像信号或从图像中明确提取的特征将分析方法分为两组，由训练模型进行分析。我们希望在最近开发的“成像”细胞分选仪中实施基于机器学习的分析时，这种分类有助于理解独特性、差异和机会。