  Early detection of cancer is crucial for treatment and overall patient
survival. In the upper aerodigestive tract (UADT) the gold standard for
identification of malignant tissue is an invasive biopsy. Recently,
non-invasive imaging techniques such as confocal laser microscopy and optical
coherence tomography (OCT) have been used for tissue assessment. In particular,
in a recent study experts classified lesions in the UADT with respect to their
invasiveness using OCT images only. As the results were promising, automatic
classification of lesions might be feasible which could assist experts in their
decision making. Therefore, we address the problem of automatic lesion
classification from OCT images. This task is very challenging as the available
dataset is extremely small and the data quality is limited. However, as similar
issues are typical in many clinical scenarios we study to what extent deep
learning approaches can still be trained and used for decision support.
