Position Papers of the 18th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2023F8069

Citation: Position Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 36, pages 59–66 (2023)

Full text

Abstract. Multispectral imaging flow cytometry (MIFC) is capable of capturing thousands of microscopic multispectral cell images per second. Deep Learning Algorithms in combination with MIFC are currently applied in different areas such as classifying blood cell morphologies, phytoplankton cells of water samples or pollen from air samples or pollinators. The goal of this work is to train classifiers for automatic and fast processing of new samples to avoid labor-intensive and error-prone manual gating and analyses and to ensure rigor of the results. In this study we compare state of the art Deep Learning architectures for the use case of multispectral image classification on datasets from three different domains to determine whether there is a suitable architecture for all applications or if a domain-specific architecture is required. Experiments have shown that there are multiple CNN architectures that show comparable results with regard to the evaluation criteria accuracy and computational effort. A single architecture that outperforms other architectures in all three domains could not be found.

References

1. S. Dunker, “Hidden Secrets Behind Dots: Improved Phytoplankton Taxonomic Resolution Using High-Throughput Imaging Flow Cytometry,” Cytometry Part A, vol. 95, no. 8, pp. 854–868, 2019, http://dx.doi.org/10.1002/cyto.a.23870.
2. S. Dunker, E. Motivans, D. Rakosy, D. Boho, P. Mäder, T. Hornick, and T. M. Knight, “Pollen analysis using multispectral imaging flow cytometry and deep learning,” New Phytologist, vol. 229, no. 1, pp. 593–606, 2021. http://dx.doi.org/10.1111/nph.16882
3. S. Dunker, M. Boyd, W. Durka, S. Erler, W. S. Harpole, S. Henning, U. Herzschuh, T. Hornick, T. Knight, S. Lips et al., “The potential of multispectral imaging flow cytometry for environmental monitoring,” Cytometry Part A, vol. 101, no. 9, pp. 782–799, 2022. http://dx.doi.org/10.1002/cyto.a.24658
4. S. Dunker, D. Boho, J. Wäldchen, and P. Mäder, “Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton,” BMC Ecology, vol. 18, no. 1, pp. 1–15, 2018. http://dx.doi.org/10.1186/s12898-018-0209-5
5. M. Doan, J. A. Sebastian, J. C. Caicedo, S. Siegert, A. Roch, T. R. Turner, O. Mykhailova, R. N. Pinto, C. McQuin, A. Goodman, M. J. Parsons, O. Wolkenhauer, H. Hennig, S. Singh, A. Wilson, J. P. Acker, P. Rees, M. C. Kolios, A. E. Carpenter, and D. Geman, “Objective assessment of stored blood quality by deep learning,” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 35, pp. 21 381–21 390, sep 2020. http://dx.doi.org/10.1073/pnas.2001227117
6. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 2261–2269, 2017. http://dx.doi.org/10.1109/CVPR.2017.243
7. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 2818–2826, 2016. http://dx.doi.org/10.1109/CVPR.2016.308
8. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, feb 2017. http://dx.doi.org/10.48550/arXiv.1602.07261 pp. 4278–4284.
9. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510–4520, 2018. http://dx.doi.org/10.1109/CVPR.2018.00474
10. K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908 LNCS, pp. 630–645, mar 2016. doi: 10.1007/978-3-319-46493-038
11. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” sep 2015. http://dx.doi.org/10.48550/arXiv.1409.1556
12. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January. Institute of Electrical and Electronics Engineers Inc., nov 2017. http://dx.doi.org/10.1109/CVPR.2017.195. ISBN 9781538604571 pp. 1800–1807.
13. P. Hofmann, A. Chatzinotas, W. S. Harpole, and S. Dunker, “Temperature and stoichiometric dependence of phytoplankton traits,” Ecology, vol. 100, no. 12, p. e02875, dec 2019. http://dx.doi.org/10.1002/ecy.2875
14. C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, 2019. http://dx.doi.org/10.1186/s40537-019-0197-0
15. X. Zeng, T. R. Martinez, X. Inchuan Zeng, and T. N. R M A Rtinez, “Distribution-balanced stratified cross-validation for accuracy estimation,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 12, no. 1, pp. 1–12, 2000. http://dx.doi.org/10.1080/095281300146272
16. S. Ruder, “An overview of gradient descent optimization algorithms,” 2017. http://dx.doi.org/10.48550/arXiv.1609.04747
17. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2017. http://dx.doi.org/10.48550/arXiv.1412.6980
18. S. Dunker, “Hidden secrets behind dots: Improved phytoplankton taxonomic resolution using high-throughput imaging flow cytometry,” Cytometry Part A, vol. 95, no. 8, pp. 854–868, 2019. http://dx.doi.org/10.1002/cyto.a.23870