# HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology

arXiv: https://arxiv.org/abs/2511.12969 · HTML: https://mlsanity.com/paper/2511.12969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 17 Nov 2025.

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

- [Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features](https://mlsanity.com/paper/2403.07592/) (2024)
- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)
- [STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics](https://mlsanity.com/paper/2406.06393/) (2024)
- [ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics](https://mlsanity.com/paper/2412.03026/) (2024)
- [ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics](https://mlsanity.com/paper/2411.16793/) (2024)
- [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)
- [Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning](https://mlsanity.com/paper/2507.16886/) (2025)
- [ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction](https://mlsanity.com/paper/2507.21516/) (2025)
- [DELST: Dual Entailment Learning for Hyperbolic Image-Gene Pretraining in Spatial Transcriptomics](https://mlsanity.com/paper/2503.00804/) (2025)

## Built on by (most cited first, 1 of 1)

- [SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference](https://mlsanity.com/paper/2609.28563/) (2026)

## Abstract (arXiv)

Spatial transcriptomics (ST) bridges gene expression and tissue morphology but faces clinical adoption barriers due to technical complexity and prohibitive costs. While computational methods predict gene expression from H&E-stained whole-slide images (WSIs), existing approaches often fail to capture the intricate biological heterogeneity within spots and are susceptible to morphological noise when integrating contextual information from surrounding tissue. To overcome these limitations, we propose HiFusion, a novel deep learning framework that integrates two complementary components. First, we introduce the Hierarchical Intra-Spot Modeling module that extracts fine-grained morphological representations through multi-resolution sub-patch decomposition, guided by a feature alignment loss to ensure semantic consistency across scales. Concurrently, we present the Context-aware Cross-scale Fusion module, which employs cross-attention to selectively incorporate biologically relevant regional context, thereby enhancing representational capacity. This architecture enables comprehensive modeling of both cellular-level features and tissue microenvironmental cues, which are essential for accurate gene expression prediction. Extensive experiments on two benchmark ST datasets demonstrate that HiFusion achieves state-of-the-art performance across both 2D slide-wise cross-validation and more challenging 3D sample-specific scenarios. These results underscore HiFusion's potential as a robust, accurate, and scalable solution for ST inference from routine histopathology.