Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 2.4% accurate classification of a cell took 68.7 3.5 ms (mean SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.

中文翻译：

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神经网络通过选择为自动显微镜提供动力的特征来快速分类生物图像

如今，人工智能在采集后分析期间用于光学显微镜中的细胞检测和分类。显微镜现在是完全自动化的，接下来有望通过根据图像做出采集决策而变得智能。它需要即时分析它们。由于准备样本和专家注释数据集的成本和时间，生物学进一步对减少的数据集进行了培训。我们通过平衡准确的检测和执行性能，提出了符合这些规范的实时图像处理。我们使用通用的高维特征提取器对图像进行了表征。然后我们使用机器学习对图像进行分类，以了解每个特征在决策和执行时间中的贡献。我们发现非线性分类器随机森林的性能优于 Fisher 的线性判别器。更重要的是，可以在不显着损失精度的情况下排除最具辨别力和最耗时的特征，从而显着增加执行时间。它表明可能与观察到的细胞的生物学相关的特征组冗余。我们提供了一种选择快速判别特征的方法。在我们的测定中，79.62.4% 准确分类一个细胞需要 68.7 3.5 ms（平均SD，嵌套在 10 个引导重复中的 5 倍交叉验证），对应于每秒 14 个细胞，分派到细胞周期的八个阶段，使用 12 个特征组并运行消费市场基于ARM的嵌入式系统。一个简单的神经网络提供了类似的性能，为更快的训练和分类铺平了道路，在通用图形处理单元上使用并行执行。最后，该策略还可用于深度神经网络，为优化这些智能显微镜算法铺平道路。