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STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics.

Jiawen Chen1, Muqing Zhou1, Wenrong Wu1

  • 1University of North Carolina at Chapel Hill.

Advances in Neural Information Processing Systems
|January 19, 2026
PubMed
Summary

A new dataset, STimage-1K4M, provides detailed gene expression data for sub-regions of pathology images. This enables deeper multi-modal analysis in computational pathology research.

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Area of Science:

  • Computational Pathology
  • Bioinformatics
  • Multi-modal Data Analysis

Background:

  • Existing medical image-text datasets lack granular detail for sub-tile regions.
  • High-level text summaries in current datasets limit in-depth analysis of pathology images.
  • There is a need for datasets linking detailed image features with genomic information.

Purpose of the Study:

  • Introduce STimage-1K4M, a novel dataset designed to provide genomic features for sub-tile pathology images.
  • Bridge the gap in current datasets by offering high-resolution spatial transcriptomics data.
  • Facilitate advanced multi-modal research in computational pathology.

Main Methods:

  • Utilized spatial transcriptomics to capture gene expression at the level of individual spatial spots.
  • Developed STimage-1K4M comprising 1,149 images from spatial transcriptomics data.
  • Paired each sub-image tile with 15,000 - 30,000 dimensional gene expression data, creating 4,293,195 pairs.

Main Results:

  • STimage-1K4M contains a large number of sub-tile image and gene expression pairs.
  • The dataset offers unprecedented granularity for analyzing pathology images.
  • Successfully linked detailed spatial information with comprehensive gene expression profiles.

Conclusions:

  • STimage-1K4M significantly enhances the potential for multi-modal data analysis in computational pathology.
  • The dataset paves the way for innovative applications requiring fine-grained image-genomic correlations.
  • This resource will accelerate research in areas demanding detailed sub-tile level insights.