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Authors: Marc Aubreville 1 ; Miguel Goncalves 2 ; Christian Knipfer 3 ; Nicolai Oetter 4 ; Tobias Würfl 1 ; Helmut Neumann 5 ; Florian Stelzle 4 ; Christopher Bohr 2 and Andreas Maier 1

Affiliations: 1 Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany ; 2 University Hospital Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany ; 3 University Medical Center Hamburg-Eppendorf and Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany ; 4 University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg and Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany ; 5 University Medical Center Mainz and Johannes Gutenberg-Universität Mainz, Germany

ISBN: 978-989-758-278-3

Keyword(s): Automatic Carcinoma Detection, Confocal Laser Endomicroscopy, Deep Convolutional Networks, Squamous Cell Carcinoma.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Confocal Microscopy ; Histology and Tissue Imaging

Abstract: Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accuracy of 89.45% and an area-under-the- curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner. (More)

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Paper citation in several formats:
Aubreville, M.; Goncalves, M.; Knipfer, C.; Oetter, N.; Würfl, T.; Neumann, H.; Stelzle, F.; Bohr, C. and Maier, A. (2018). Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 27-34. DOI: 10.5220/0006534700270034

@conference{bioimaging18,
author={Marc Aubreville. and Miguel Goncalves. and Christian Knipfer. and Nicolai Oetter. and Tobias Würfl. and Helmut Neumann. and Florian Stelzle. and Christopher Bohr. and Andreas Maier.},
title={Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2018},
pages={27-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006534700270034},
isbn={978-989-758-278-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment
SN - 978-989-758-278-3
AU - Aubreville, M.
AU - Goncalves, M.
AU - Knipfer, C.
AU - Oetter, N.
AU - Würfl, T.
AU - Neumann, H.
AU - Stelzle, F.
AU - Bohr, C.
AU - Maier, A.
PY - 2018
SP - 27
EP - 34
DO - 10.5220/0006534700270034

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