STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics
<|reference_start|>STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics: 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.<|reference_end|>
@article{chen2024stimage-1k4m:,
    title={STimage-1K4M: A histopathology image-gene expression dataset for spatial
  transcriptomics},
    author={Jiawen Chen, Muqing Zhou, Wenrong Wu, Jinwei Zhang, Yun Li, Didong Li},
    journal={arXiv preprint arXiv:2406.06393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06393},
    primaryClass={cs.CV cs.CL q-bio.GN}
}