  The surgical removal of head and neck tumors requires safe margins, which are
usually confirmed intraoperatively by means of frozen sections. This method is,
in itself, an oversampling procedure, which has a relatively low sensitivity
compared to the definitive tissue analysis on paraffin-embedded sections.
Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has
shown its potential in the live optical biopsy of tissue. An automated analysis
of this notoriously difficult to interpret modality would help surgeons.
However, the images of CLE show a wide variability of patterns, caused both by
individual factors but also, and most strongly, by the anatomical structures of
the imaged tissue, making it a challenging pattern recognition task. In this
work, we evaluate four popular few shot learning (FSL) methods towards their
capability of generalizing to unseen anatomical domains in CLE images. We
evaluate this on images of sinunasal tumors (SNT) from five patients and on
images of the vocal folds (VF) from 11 patients using a cross-validation
scheme. The best respective approach reached a median accuracy of 79.6% on the
rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT
dataset. Our results indicate that FSL on CLE images is viable, but strongly
affected by the number of patients, as well as the diversity of anatomical
patterns.
