Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Buchkapitel |
| DOI | 10.15439/2023F8069 |
| Titel (primär) | Comparison of deep learning architectures for three different multispectral imaging flow cytometry datasets |
| Titel (sekundär) | Position Papers of the of the 18th Conference on Computer Science and Intelligence Systems, Warsaw, Poland, 17–20 September, 2023 |
| Autor | Krajsic, P.; Hornick, T.; Dunker, S. |
| Herausgeber | Ganzha, M.; Maciaszek, L.; Paprzycki, M.; Ślęzak, D. |
| Quelle | Annals of Computer Science and Information Systems |
| Erscheinungsjahr | 2023 |
| Department | PHYDIV |
| Band/Volume | 36 |
| Seite von | 59 |
| Seite bis | 66 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Computer Vision; Classification; Deep Learning; Multispectral Imaging Flow Cytometry |
| 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 Convolutional Neural Network (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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=28051 |
| Krajsic, P., Hornick, T., Dunker, S. (2023): Comparison of deep learning architectures for three different multispectral imaging flow cytometry datasets In: Ganzha, M., Maciaszek, L., Paprzycki, M., Ślęzak, D. (eds.) Position Papers of the of the 18th Conference on Computer Science and Intelligence Systems, Warsaw, Poland, 17–20 September, 2023 Annals of Computer Science and Information Systems 36 Polish Information Processing Society, Warsaw, p. 59 - 66 10.15439/2023F8069 |
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