  Novel imaging technologies raise many questions concerning the adaptation of
computer-aided decision support systems. Classification models either need to
be adapted or even newly trained from scratch to exploit the full potential of
enhanced techniques. Both options typically require the acquisition of new
labeled training data. In this work we investigate the applicability of
image-to-image translation to endoscopic images showing different imaging
modalities, namely conventional white-light and narrow-band imaging. In a study
on computer-aided celiac disease diagnosis, we explore whether image-to-image
translation is capable of effectively performing the translation between the
domains. We investigate if models can be trained on virtual (or a mixture of
virtual and real) samples to improve overall accuracy in a setting with limited
labeled training data. Finally, we also ask whether a translation of testing
images to another domain is capable of improving accuracy by exploiting the
enhanced imaging characteristics.
