# Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning

arXiv: https://arxiv.org/abs/2306.01859 · HTML: https://mlsanity.com/paper/2306.01859/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer](https://mlsanity.com/paper/2009.08917/) (2020)
- [hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images](https://mlsanity.com/paper/2304.04507/) (2023)

## Built on by (most cited first, 30 of 79)

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features](https://mlsanity.com/paper/2403.07592/) (2024)
- [Transcriptomics-guided Slide Representation Learning in Computational Pathology](https://mlsanity.com/paper/2405.11618/) (2024)
- [Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images](https://mlsanity.com/paper/2501.15598/) (2025)
- [Multimodal contrastive learning for spatial gene expression prediction using histology images](https://mlsanity.com/paper/2407.08216/) (2024)
- [STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics](https://mlsanity.com/paper/2406.06393/) (2024)
- [Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching](https://mlsanity.com/paper/2506.05361/) (2025)
- [CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale](https://mlsanity.com/paper/2405.17537/) (2024)
- [MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images](https://mlsanity.com/paper/2412.02601/) (2024)
- [ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics](https://mlsanity.com/paper/2412.03026/) (2024)
- [Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.14195/) (2024)
- [Domain-specific optimization and diverse evaluation of self-supervised models for histopathology](https://mlsanity.com/paper/2310.13259/) (2023)
- [MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics](https://mlsanity.com/paper/2502.21011/) (2025)
- [A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics](https://mlsanity.com/paper/2508.01490/) (2025)
- [Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning](https://mlsanity.com/paper/2506.23827/) (2025)
- [Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics](https://mlsanity.com/paper/2412.04072/) (2024)
- [Img2ST-Net: Efficient High-Resolution Spatial Omics Prediction from Whole Slide Histology Images via Fully Convolutional Image-to-Image Learning](https://mlsanity.com/paper/2508.14393/) (2025)
- [HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction](https://mlsanity.com/paper/2601.21560/) (2026)
- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation](https://mlsanity.com/paper/2506.21107/) (2025)
- [PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2503.16816/) (2025)
- [Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking](https://mlsanity.com/paper/2505.02980/) (2025)
- [Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning](https://mlsanity.com/paper/2506.08854/) (2025)
- [SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space](https://mlsanity.com/paper/2506.21857/) (2025)
- [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)
- [A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction](https://mlsanity.com/paper/2604.03630/) (2026)
- [Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction](https://mlsanity.com/paper/2406.06517/) (2024)
- [SpaRED benchmark: Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion](https://mlsanity.com/paper/2407.13027/) (2024)

## Abstract (arXiv)

Histology imaging is an important tool in medical diagnosis and research, enabling the examination of tissue structure and composition at the microscopic level. Understanding the underlying molecular mechanisms of tissue architecture is critical in uncovering disease mechanisms and developing effective treatments. Gene expression profiling provides insight into the molecular processes underlying tissue architecture, but the process can be time-consuming and expensive. We present BLEEP (Bi-modaL Embedding for Expression Prediction), a bi-modal embedding framework capable of generating spatially resolved gene expression profiles of whole-slide Hematoxylin and eosin (H&E) stained histology images. BLEEP uses contrastive learning to construct a low-dimensional joint embedding space from a reference dataset using paired image and expression profiles at micrometer resolution. With this approach, the gene expression of any query image patch can be imputed using the expression profiles from the reference dataset. We demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking its performance on a human liver tissue dataset captured using the 10x Visium platform, where it achieves significant improvements over existing methods. Our results demonstrate the potential of BLEEP to provide insights into the molecular mechanisms underlying tissue architecture, with important implications in diagnosis and research of various diseases. The proposed approach can significantly reduce the time and cost associated with gene expression profiling, opening up new avenues for high-throughput analysis of histology images for both research and clinical applications.