STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929391789932544 |
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| author | Chen, Jiawen Zhou, Muqing Wu, Wenrong Zhang, Jinwei Li, Yun Li, Didong |
| author_facet | Chen, Jiawen Zhou, Muqing Wu, Wenrong Zhang, Jinwei Li, Yun Li, Didong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_06393 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics Chen, Jiawen Zhou, Muqing Wu, Wenrong Zhang, Jinwei Li, Yun Li, Didong Computer Vision and Pattern Recognition Computation and Language Genomics I.4.10; I.2.10 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. |
| title | STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics |
| topic | Computer Vision and Pattern Recognition Computation and Language Genomics I.4.10; I.2.10 |
| url | https://arxiv.org/abs/2406.06393 |