Deep learning to detect bacterial colonies for the production of vaccines
| Autor: | Paul Smyth, John Aldo Lee, Thomas Beznik, Gaël de Lannoy |
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| Přispěvatelé: | UCL - SSS/IREC/MIRO - Pôle d'imagerie moléculaire, radiothérapie et oncologie |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Cognitive Neuroscience Deep learning Computer Science - Computer Vision and Pattern Recognition Pattern recognition Quantitative Biology - Quantitative Methods Machine Learning (cs.LG) Computer Science Applications Artificial Intelligence FOS: Biological sciences Segmentation Artificial intelligence business Quantitative Methods (q-bio.QM) Bacterial colony |
| Zdroj: | Scopus-Elsevier Neurocomputing, Vol. 470, no.1, p. 427-431 (2022) |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2021.04.130 |
| Popis: | During the development of vaccines, bacterial colony forming units (CFUs) are counted in order to quantify the yield in the fermentation process. This manual task is time-consuming and error-prone. In this work we test multiple segmentation algorithms based on the U-Net CNN architecture and show that these offer robust, automated CFU counting. We show that the multiclass generalisation with a bespoke loss function allows distinguishing virulent and avirulent colonies with acceptable accuracy. While many possibilities are left to explore, our results show the potential of deep learning for separating and classifying bacterial colonies. 6 pages, 2 figures, accepted at ESANN 2020 (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning) |
| Databáze: | OpenAIRE |
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