# All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning

arXiv: https://arxiv.org/abs/2108.10446 · HTML: https://mlsanity.com/paper/2108.10446/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data](https://mlsanity.com/paper/2107.00648/) (2021)
- [Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading](https://mlsanity.com/paper/2012.05197/) (2020)

## Built on by (most cited first, 7 of 7)

- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks](https://mlsanity.com/paper/2407.08625/) (2024)
- [NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful](https://mlsanity.com/paper/2409.11546/) (2024)
- [Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits](https://mlsanity.com/paper/2112.09496/) (2021)
- [Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection](https://mlsanity.com/paper/2511.18336/) (2025)
- [ISG: I can See Your Gene Expression](https://mlsanity.com/paper/2210.16728/) (2022)

## Abstract (arXiv)

"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?" In this work, we propose a "stain-aware" machine learning approach for prediction of spatial transcriptomic gene expression profiles using digital pathology image of a routine Hematoxylin & Eosin (H&E) histology section. Unlike recent deep learning methods which are used for gene expression prediction, our proposed approach termed Neural Stain Learning (NSL) explicitly models the association of stain absorption characteristics of the tissue with gene expression patterns in spatial transcriptomics by learning a problem-specific stain deconvolution matrix in an end-to-end manner. The proposed method with only 11 trainable weight parameters outperforms both classical regression models with cellular composition and morphological features as well as deep learning methods. We have found that the gene expression predictions from the proposed approach show higher correlations with true expression values obtained through sequencing for a larger set of genes in comparison to other approaches.