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Authors: Jakub Paplhám 1 ; Vojtěch Franc 1 and Daniela Lžičařová 2

Affiliations: 1 Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic ; 2 Second Faculty of Medicine, Charles University, Prague, Czech Republic

Keyword(s): Filamentous Fungi, Yeast, Automated Detection, Fluorescence Staining, Poisson Image Editing.

Abstract: Early detection of yeast and filamentous fungi in clinical samples is critical in treating patients predisposed to severe infections caused by these organisms. The patients undergo regular screening, and the gathered samples are manually examined by trained personnel. This work uses deep neural networks to detect filamentous fungi and yeast in the clinical samples to simplify the work of the human operator by filtering out samples that are clearly negative and presenting the operator with only samples suspected of containing the contaminant. We propose data augmentation with Poisson inpainting and compare the model performance against expert and beginner-level humans. The method achieves human-level performance, theoretically reducing the amount of manual labor by 87%, given a true positive rate of 99% and incidence rate of 10%.

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Paper citation in several formats:
Paplhám, J.; Franc, V. and Lžičařová, D. (2023). Detection of Microscopic Fungi and Yeast in Clinical Samples Using Fluorescence Microscopy and Deep Learning. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 777-784. DOI: 10.5220/0011616100003417

@conference{visapp23,
author={Jakub Paplhám. and Vojtěch Franc. and Daniela Lžiča\v{r}ová.},
title={Detection of Microscopic Fungi and Yeast in Clinical Samples Using Fluorescence Microscopy and Deep Learning},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={777-784},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011616100003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Detection of Microscopic Fungi and Yeast in Clinical Samples Using Fluorescence Microscopy and Deep Learning
SN - 978-989-758-634-7
IS - 2184-4321
AU - Paplhám, J.
AU - Franc, V.
AU - Lžičařová, D.
PY - 2023
SP - 777
EP - 784
DO - 10.5220/0011616100003417
PB - SciTePress

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