# Ink removal from histopathology whole slide images by combining classification, detection and image generation models

arXiv: https://arxiv.org/abs/1905.04385 · HTML: https://mlsanity.com/paper/1905.04385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 3 of 3)

- [Artifact Removal in Histopathology Images](https://mlsanity.com/paper/2211.16161/) (2022)
- [Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis](https://mlsanity.com/paper/2410.00152/) (2024)
- [Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images](https://mlsanity.com/paper/2410.03289/) (2024)

## Abstract (arXiv)

Histopathology slides are routinely marked by pathologists using permanent ink markers that should not be removed as they form part of the medical record. Often tumour regions are marked up for the purpose of highlighting features or other downstream processing such an gene sequencing. Once digitised there is no established method for removing this information from the whole slide images limiting its usability in research and study. Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner. We propose an efficient pipeline using convolution neural networks that results in ink-free images without compromising information and image resolution. Our pipeline includes a sequential classical convolution neural network for accurate classification of contaminated image tiles, a fast region detector and a domain adaptive cycle consistent adversarial generative model for restoration of foreground pixels. Both quantitative and qualitative results on four different whole slide images show that our approach yields visually coherent ink-free whole slide images.