ArticleAdvances in neural information processing systems2024
STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics.
Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Spatial and Single-Cell Multi-Omics Analyses of T-Cell Subsets and Interactions in Melanoma Lymph Node Metastasis.Experimental dermatology · 2026Review
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- Deep learning of pretreatment ascites cytopathology for platinum-resistance risk stratification in advanced epithelial ovarian cancer.Neoplasia (New York, N.Y.) · 2026Article
- Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts.Genome medicine · 2026Article
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.bioRxiv : the preprint server for biology · 2026Article
- Tumors hijack immune-privileging regulons via distinct cell types to confer T cell desertion and immunotherapy resistance across various cancers.Nature communications · 2026Article
- IMIREG: a lineage-resolved regulon signature unveiling immune engagement archetypes and predicting immunotherapy response across diverse cancers.NPJ precision oncology · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.Scientific data · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- sCellST predicts single-cell gene expression from H& E images.Nature communications · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images.NPJ digital medicine · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Spatial omics enters the microscopic realm: opportunities and challenges.Trends in genetics : TIG · 2025Review
- spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language Model.bioRxiv : the preprint server for biology · 2025Article
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6 authors.
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Abstract
Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.