  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.
