# Artifact Removal in Histopathology Images

arXiv: https://arxiv.org/abs/2211.16161 · HTML: https://mlsanity.com/paper/2211.16161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention](https://mlsanity.com/paper/2007.08373/) (2020)
- [Region-guided CycleGANs for Stain Transfer in Whole Slide Images](https://mlsanity.com/paper/2208.12847/) (2022)
- [Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks](https://mlsanity.com/paper/1811.02642/) (2018)
- [Ink removal from histopathology whole slide images by combining classification, detection and image generation models](https://mlsanity.com/paper/1905.04385/) (2019)

## Built on by (most cited first, 1 of 1)

- [ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis](https://mlsanity.com/paper/2304.01053/) (2023)

## 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.