Artifact Removal in Histopathology Images

• URL: http://arxiv.org/abs/2211.16161v1
• Date: Tue, 29 Nov 2022 12:44:45 GMT
• Title: Artifact Removal in Histopathology Images
• Authors: Cameron Dahan, Stergios Christodoulidis, Maria Vakalopoulou, Joseph Boyd
• Abstract summary: Image-to-image translation networks such as CycleGANs are capable of learning an artifact removal function from unpaired data. We identify a surjection problem with artifact removal, and propose a weakly-supervised extension to CycleGAN to address this. We assemble a pan-cancer dataset comprising artifact and clean tiles from the TCGA database.
• Score: 2.973752436440099
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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