Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from
  Duplex to Monoplex IHC Images
Nicolas Brieu Nicolas Triltsch Philipp Wortmann Dominik Winter Shashank Saran Marlon Rebelatto Günter Schmidt
Generative models enable the translation from a source image domain where
readily trained models are available to a target domain unseen during training.
While Cycle Generative Adversarial Networks (GANs) are well established, the
associated cycle consistency constrain relies on that an invertible mapping
exists between the two domains. This is, however, not the case for the
translation between images stained with chromogenic monoplex and duplex
immunohistochemistry (IHC) assays. Focusing on the translation from the latter
to the first, we propose - through the introduction of a novel training design,
an alternative constrain leveraging a set of immunofluorescence (IF) images as
an auxiliary unpaired image domain. Quantitative and qualitative results on a
downstream segmentation task show the benefit of the proposed method in
comparison to baseline approaches.