  In the clinical setting of histopathology, whole-slide image (WSI) artifacts
frequently arise, distorting regions of interest, and having a pernicious
impact on WSI analysis. Image-to-image translation networks such as CycleGANs
are in principle capable of learning an artifact removal function from unpaired
data. However, we identify a surjection problem with artifact removal, and
propose an weakly-supervised extension to CycleGAN to address this. We assemble
a pan-cancer dataset comprising artifact and clean tiles from the TCGA
database. Promising results highlight the soundness of our method.
