# M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images

arXiv: https://arxiv.org/abs/2409.15092 · HTML: https://mlsanity.com/paper/2409.15092/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation](https://mlsanity.com/paper/2304.08506/) (2023)
- [Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features](https://mlsanity.com/paper/2403.07592/) (2024)
- [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)
- [DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks](https://mlsanity.com/paper/1907.11065/) (2019)

## Built on by (most cited first, 13 of 13)

- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2510.04315/) (2025)
- [Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics](https://mlsanity.com/paper/2512.06612/) (2025)
- [Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images](https://mlsanity.com/paper/2603.19766/) (2026)
- [Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics](https://mlsanity.com/paper/2511.17685/) (2025)
- [Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images](https://mlsanity.com/paper/2507.14670/) (2025)
- [CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics](https://mlsanity.com/paper/2508.10232/) (2025)
- [HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction](https://mlsanity.com/paper/2511.22107/) (2025)
- [Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology](https://mlsanity.com/paper/2602.13944/) (2026)
- [Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images](https://mlsanity.com/paper/2608.00405/) (2026)
- [Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics](https://mlsanity.com/paper/2608.14710/) (2026)
- [Correlation-Guided Flow Matching with Annealed Masking for Spatial Transcriptomics Generation](https://mlsanity.com/paper/2609.22187/) (2026)
- [GATE-ST: Gene-Aware Text-image Encoder for Spatial Transcriptomics](https://mlsanity.com/paper/2609.38690/) (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 along with patch-sampling for this task, which ignores the inherent multi-scale information embedded in the pyramidal data structure of digital pathology images, and wastes the inter-spot visual information crucial for accurate gene expression prediction. To address these limitations, we propose M2OST, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images via a decoupled multi-scale feature extractor. Unlike traditional models that are trained with one-to-one image-label pairs, M2OST uses multiple images from different levels of the digital pathology image to jointly predict the gene expressions in their common corresponding spot. Built upon our many-to-one scheme, M2OST can be easily scaled to fit different numbers of inputs, and its network structure inherently incorporates nearby inter-spot features, enhancing regression performance. We have tested M2OST on three public ST datasets and the experimental results show that M2OST can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs).