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Artifact Removal in Histopathology Images
- Publication Year :
- 2022
-
Abstract
- 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.<br />Comment: Corrected typos, small modification of Figure 1 (+ reflected in Section 2.1), results unchanged
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2211.16161
- Document Type :
- Working Paper