Authors:
Marc Aubreville
1
;
Miguel Goncalves
2
;
Christian Knipfer
3
;
1
;
Nicolai Oetter
2
;
1
;
Tobias Würfl
1
;
Helmut Neumann
4
;
Florian Stelzle
2
;
1
;
Christopher Bohr
2
and
Andreas Maier
1
Affiliations:
1
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
;
2
University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
;
3
University Medical Center Hamburg-Eppendorf, Germany
;
4
University Medical Center Mainz, Johannes Gutenberg-Universität Mainz, Germany
Keyword(s):
Automatic Carcinoma Detection, Confocal Laser Endomicroscopy, Deep Convolutional Networks, Squamous Cell Carcinoma.
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 accurac
y 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.
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