QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 31 May 2024.
Builds on
8 of 30 references.
References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
- 2018Cell Detection with Star-convex Polygonscited by 182
- 2023Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualizationcited by 7
- 2023Open and reusable deep learning for pathology with WSInfer and QuPathcited by 3
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.