# M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images

arXiv: https://arxiv.org/abs/2401.10608 · HTML: https://mlsanity.com/paper/2401.10608/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction](https://mlsanity.com/paper/2307.11952/) (2023)
- [OCELOT: Overlapped Cell on Tissue Dataset for Histopathology](https://mlsanity.com/paper/2303.13110/) (2023)

## Built on by (most cited first, 8 of 8)

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images](https://mlsanity.com/paper/2501.15598/) (2025)
- [What makes for good morphology representations for spatial omics?](https://mlsanity.com/paper/2407.20660/) (2024)
- [Teacher-Student Guided Inverse Modeling for Steel Final Hardness Estimation](https://mlsanity.com/paper/2510.05402/) (2025)
- [DriftST: One-Step Generative Inference of Spatial Transcriptomics from H&E Histology](https://mlsanity.com/paper/2607.04740/) (2026)
- [STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images](https://mlsanity.com/paper/2609.05956/) (2026)
- [Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction](https://mlsanity.com/paper/2609.33928/) (2026)
- [Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images](https://mlsanity.com/paper/2609.36429/) (2026)

## Abstract (arXiv)

The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost remains expensive. Therefore, directly predicting the ST expressions from digital pathology images is desired. Current methods usually adopt existing regression backbones for this task, which ignore the inherent multi-scale hierarchical data structure of digital pathology images. To address this limit, we propose M2ORT, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images through a decoupled multi-scale feature extractor. Different from traditional models that are trained with one-to-one image-label pairs, M2ORT accepts multiple pathology images of different magnifications at a time to jointly predict the gene expressions at their corresponding common ST spot, aiming at learning a many-to-one relationship through training. We have tested M2ORT on three public ST datasets and the experimental results show that M2ORT can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs). The code is available at: https://github.com/Dootmaan/M2ORT/.