A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis

The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images and the high variation of cells from different sources. Herein, we propose a meta-learning approach for multicenter and small-data single-cell image analysis. Meta-learning combines automated wide-field fluorescence microscopy to build a hardware and software system to analyze cellular heterogeneity. We verified that the meta-learning single-cell imaging platform extracts the relevant information between multiple data centers through training to reduce the need for workload required to label single-cell images. The results show that the classification accuracy of the target task can reach about 92% using only 60% data volume labeled single-cell images. However, to achieve the same recognition accuracy, we need to use 100% data volume labeled single-cell images for traditional deep learning. Moreover, the accuracy achieved by our platform surpasses that of traditional deep learning methods, even when the data volume is reduced to 5%, which means our platform can significantly reduce the volume of single-cell image data labeling and the manual data labeling workload, thereby enhancing work efficiency and reducing work costs. Furthermore, our platform's robustness against data from different sources of single-cell images has been verified through knowledge migration experiments on public data sets. This robustness should instill confidence in the applicability of our platform across various research settings and data sources.