Artifact Removal in Histopathology Images
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 29 Nov 2022.
Builds on
8 of 13 references.
References · 8
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2016Least Squares Generative Adversarial Networkscited by 1,000
- 2018Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Imagescited by 55
- 2020Self-Supervised Nuclei Segmentation in Histopathological Images Using Attentioncited by 30
- 2022Region-guided CycleGANs for Stain Transfer in Whole Slide Imagescited by 8
- 2018Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networkscited by 6
- 2019Ink removal from histopathology whole slide images by combining classification, detection and image generation modelscited by 3
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Citing papers per year: 2023: 1.
1 paper.