# Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics

arXiv: https://arxiv.org/abs/2412.00651 · HTML: https://mlsanity.com/paper/2412.00651/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 85 references that are in the map.
- First posted on arXiv 1 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model](https://mlsanity.com/paper/2407.15362/) (2024)
- [Multimodal Prototyping for cancer survival prediction](https://mlsanity.com/paper/2407.00224/) (2024)
- [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)

## Built on by (most cited first, 5 of 5)

- [SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model](https://mlsanity.com/paper/2510.12709/) (2025)
- [GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction](https://mlsanity.com/paper/2510.04315/) (2025)
- [DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction](https://mlsanity.com/paper/2605.14104/) (2026)
- [Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology](https://mlsanity.com/paper/2602.13944/) (2026)
- [PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining](https://mlsanity.com/paper/2608.14924/) (2026)

## Abstract (arXiv)

Recent advancements in multimodal pre-training models have significantly advanced computational pathology. However, current approaches predominantly rely on visual-language models, which may impose limitations from a molecular perspective and lead to performance bottlenecks. Here, we introduce a Unified Molecule-enhanced Pathology Image REpresentationn Learning framework (UMPIRE). UMPIRE aims to leverage complementary information from gene expression profiles to guide the multimodal pre-training, enhancing the molecular awareness of pathology image representation learning. We demonstrate that this molecular perspective provides a robust, task-agnostic training signal for learning pathology image embeddings. Due to the scarcity of paired data, approximately 4 million entries of spatial transcriptomics gene expression were collected to train the gene encoder. By leveraging powerful pre-trained encoders, UMPIRE aligns the encoders across over 697K pathology image-gene expression pairs. The performance of UMPIRE is demonstrated across various molecular-related downstream tasks, including gene expression prediction, spot classification, and mutation state prediction in whole slide images. Our findings highlight the effectiveness of multimodal data integration and open new avenues for exploring computational pathology enhanced by molecular perspectives. The code and pre-trained weights are available at https://github.com/Hanminghao/UMPIRE.