  Gastrointestinal (GI) cancer precursors require frequent monitoring for risk
stratification of patients. Automated segmentation methods can help to assess
risk areas more accurately, and assist in therapeutic procedures or even
removal. In clinical practice, addition to the conventional white-light imaging
(WLI), complimentary modalities such as narrow-band imaging (NBI) and
fluorescence imaging are used. While, today most segmentation approaches are
supervised and only concentrated on a single modality dataset, this work
exploits to use a target-independent unsupervised domain adaptation (UDA)
technique that is capable to generalize to an unseen target modality. In this
context, we propose a novel UDA-based segmentation method that couples the
variational autoencoder and U-Net with a common EfficientNet-B4 backbone, and
uses a joint loss for latent-space optimization for target samples. We show
that our model can generalize to unseen target NBI (target) modality when
trained using only WLI (source) modality. Our experiments on both upper and
lower GI endoscopy data show the effectiveness of our approach compared to
naive supervised approach and state-of-the-art UDA segmentation methods.
