# High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE

arXiv: https://arxiv.org/abs/2407.20518 · HTML: https://mlsanity.com/paper/2407.20518/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Multimodal contrastive learning for spatial gene expression prediction using histology images](https://mlsanity.com/paper/2407.08216/) (2024)
- [SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq](https://mlsanity.com/paper/2407.13182/) (2024)
- [stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement](https://mlsanity.com/paper/2407.08224/) (2024)

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

- [CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis](https://mlsanity.com/paper/2602.21637/) (2026)
- [Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction](https://mlsanity.com/paper/2508.17389/) (2025)
- [PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining](https://mlsanity.com/paper/2608.14924/) (2026)

## Abstract (arXiv)

Spatial transcriptomics (ST) is a groundbreaking genomic technology that enables spatial localization analysis of gene expression within tissue sections. However, it is significantly limited by high costs and sparse spatial resolution. An alternative, more cost-effective strategy is to use deep learning methods to predict high-density gene expression profiles from histological images. However, existing methods struggle to capture rich image features effectively or rely on low-dimensional positional coordinates, making it difficult to accurately predict high-resolution gene expression profiles. To address these limitations, we developed HisToSGE, a method that employs a Pathology Image Large Model (PILM) to extract rich image features from histological images and utilizes a feature learning module to robustly generate high-resolution gene expression profiles. We evaluated HisToSGE on four ST datasets, comparing its performance with five state-of-the-art baseline methods. The results demonstrate that HisToSGE excels in generating high-resolution gene expression profiles and performing downstream tasks such as spatial domain identification. All code and public datasets used in this paper are available at https://github.com/wenwenmin/HisToSGE and https://zenodo.org/records/12792163.