# QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis

arXiv: https://arxiv.org/abs/2406.01613 · HTML: https://mlsanity.com/paper/2406.01613/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization](https://mlsanity.com/paper/2304.04142/) (2023)
- [Open and reusable deep learning for pathology with WSInfer and QuPath](https://mlsanity.com/paper/2309.04631/) (2023)

## Built on by (most cited first, 2 of 2)

- [QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis](https://mlsanity.com/paper/2406.14307/) (2024)
- [PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2503.16284/) (2025)

## 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.