QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913578440720384 |
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| author | Huang, Chao-Hui Lichtarge, Sara Fernandez, Diane |
| author_facet | Huang, Chao-Hui Lichtarge, Sara Fernandez, Diane |
| contents | The integration of AI in digital pathology, particularly in whole slide image (WSI) and spatial transcriptomics (ST) analysis, holds immense potential for enhancing our understanding of diseases. Despite challenges such as training pattern preparation and resolution disparities, the convergence of these technologies can unlock new insights. We introduce QuST, a tool that bridges the gap between WSI and ST, underscoring the transformative power of this integrated approach in disease biology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01613 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis Huang, Chao-Hui Lichtarge, Sara Fernandez, Diane Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing The integration of AI in digital pathology, particularly in whole slide image (WSI) and spatial transcriptomics (ST) analysis, holds immense potential for enhancing our understanding of diseases. Despite challenges such as training pattern preparation and resolution disparities, the convergence of these technologies can unlock new insights. We introduce QuST, a tool that bridges the gap between WSI and ST, underscoring the transformative power of this integrated approach in disease biology. |
| title | QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2406.01613 |