Artifact Removal in Histopathology Images
This addresses artifact removal in histopathology for clinical settings, but it is incremental as it builds on existing CycleGAN methods.
The paper tackled the problem of artifact removal in histopathology images, which distorts regions of interest and impacts analysis, by proposing a weakly-supervised extension to CycleGAN to address a surjection problem, achieving promising results on a pan-cancer dataset from TCGA.
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.