Exploring semantic consistency in unpaired image translation to generate data for surgical applications

Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer, Fiona R. Kolbinger, Marius Distler, Jürgen Weitz, Stefanie Speidel

Abstract: In surgical computer vision applications, data privacy and expert annotation challenges impede the acquisition of labeled training data. Unpaired image-to-image translation techniques have been explored to automatically generate annotated datasets by translating synthetic images into a realistic domain. The preservation of structure and semantic consistency, i.e., per-class distribution during translation, poses a significant challenge, particularly in cases of semantic distributional mismatch.