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. 2017 Sep 14;7:11651. doi: 10.1038/s41598-017-11534-0

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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(a) Scatter plot of cytometry result of mixed-size bead suspension. t-SNE embedding was applied to reduce the high dimensional feature map to two dimensions for visualization. The output of the last hidden layer has originally 300 dimension, and t-SNE embedded its dimension to depict the classification result. Each axis represents the latent space (z) variable embedded by t-SNE. Point colors are labelled according to its classes: 7 μm (red): 1,540 points, 10 μm (green): 549 points, 15 μm (blue): 257 points. (b) Cropped single-bead images and their classification results. Some cropped single-cell images include another bead (doublet; red boxes) but the classifier predict category of the object near the centre position with high probability. This property can filter out doublet. In addition, cyan box shows a case that the object is not located at the centre position but classifier can predict its class due to the invariance of convolutional neural networks to small translation.