# Fast GPU-Enabled Color Normalization for Digital Pathology

arXiv: https://arxiv.org/abs/1901.03088 · HTML: https://mlsanity.com/paper/1901.03088/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection](https://mlsanity.com/paper/2108.09178/) (2021)
- [EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning](https://mlsanity.com/paper/2208.12506/) (2022)
- [Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)](https://mlsanity.com/paper/2107.05426/) (2021)
- [CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2304.09623/) (2023)
- [Classification of lung cancer subtypes on CT images with synthetic pathological priors](https://mlsanity.com/paper/2308.04663/) (2023)
- [Intrinsic Bias Identification on Medical Image Datasets](https://mlsanity.com/paper/2203.12872/) (2022)
- [ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization](https://mlsanity.com/paper/2305.06511/) (2023)
- [CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images](https://mlsanity.com/paper/2407.13092/) (2024)

## Abstract (arXiv)

Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the several popular techniques for color normalization, structure preserving color normalization (SPCN) is well-motivated, convincingly tested, and published with its code base. However, SPCN makes occasional errors in color basis estimation leading to artifacts such as swapping the color basis vectors between stains or giving a colored tinge to the background with no tissue. We made several algorithmic improvements to remove these artifacts. Additionally, the original SPCN code is not readily usable on gigapixel whole slide images (WSIs) due to long run times, use of proprietary software platform and libraries, and its inability to automatically handle WSIs. We completely rewrote the software such that it can automatically handle images of any size in popular WSI formats. Our software utilizes GPU-acceleration and open-source libraries that are becoming ubiquitous with the advent of deep learning. We also made several other small improvements and achieved a multifold overall speedup on gigapixel images. Our algorithm and software is usable right out-of-the-box by the computational pathology community.