# HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation

arXiv: https://arxiv.org/abs/2508.07225 · HTML: https://mlsanity.com/paper/2508.07225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16322/) (2023)
- [Arbitrary Video Style Transfer via Multi-Channel Correlation](https://mlsanity.com/paper/2009.08003/) (2020)
- [Diffusion Models, Image Super-Resolution And Everything: A Survey](https://mlsanity.com/paper/2401.00736/) (2024)
- [DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2303.13933/) (2023)
- [Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis](https://mlsanity.com/paper/2406.03002/) (2024)
- [Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics](https://mlsanity.com/paper/2404.12973/) (2024)
- [Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning](https://mlsanity.com/paper/2406.13979/) (2024)
- [Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling](https://mlsanity.com/paper/2505.10729/) (2025)
- [Score-based Diffusion Model for Unpaired Virtual Histology Staining](https://mlsanity.com/paper/2506.23184/) (2025)

## Abstract (arXiv)

Spatial transcriptomics (ST) reveals spatial heterogeneity of gene expression, yet its resolution is limited by current platforms. Recent methods enhance resolution via H&E-stained histology, but three major challenges persist: (1) isolating expression-relevant features from visually complex H&E images; (2) achieving spatially precise multimodal alignment in diffusion-based frameworks; and (3) modeling gene-specific variation across expression channels. We propose HaDM-ST (Histology-assisted Differential Modeling for ST Generation), a high-resolution ST generation framework conditioned on H&E images and low-resolution ST. HaDM-ST includes: (i) a semantic distillation network to extract predictive cues from H&E; (ii) a spatial alignment module enforcing pixel-wise correspondence with low-resolution ST; and (iii) a channel-aware adversarial learner for fine-grained gene-level modeling. Experiments on 200 genes across diverse tissues and species show HaDM-ST consistently outperforms prior methods, enhancing spatial fidelity and gene-level coherence in high-resolution ST predictions.