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

Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors