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

• URL: http://arxiv.org/abs/2508.07225v1
• Date: Sun, 10 Aug 2025 08:09:06 GMT
• Title: HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation
• Abstract summary: 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.<n> 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.
• Score: 3.0708458805558347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• DriftST: One-Step Generative Inference of Spatial Transcriptomics from H\&E Histology [11.977375064177354]
  Inferring expression directly from Hematoxylin and Eosin stained histology offers a cost-effective alternative.<n>We propose DriftST, a unified framework for inferring spatially resolved gene expression from H&E images.<n> operating on a generic gene-panel representation, DriftST applies directly to both spot-level and cell-level data in one framework.
  arXiv  Detail & Related papers  (2026-07-06T07:22:58Z)
• DSA-SRGS: Super-Resolution Gaussian Splatting for Dynamic Sparse-View DSA Reconstruction [67.42242016220122]
  Digital subtraction angiography is a key imaging technique for the auxiliary diagnosis and treatment of cerebrovascular diseases.<n>Recent advancements in gaussian splatting and dynamic neural representations have enabled robust 3D vessel reconstruction from sparse dynamic inputs.<n>This paper proposes DSA-SRGS, the first super-resolution gaussian splatting framework for dynamic sparse-view DSA reconstruction.
  arXiv  Detail & Related papers  (2026-03-05T03:41:08Z)
• HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology [7.982889842329205]
  HiFusion is a novel deep learning framework that integrates two complementary components.<n>We show that HiFusion achieves state-of-the-art performance across both 2D slide-wise cross-validation and more challenging 3D sample-specific scenarios.<n>These results underscore HiFusion's potential as a robust, accurate, and scalable solution for ST inference from routine histopathology.
  arXiv  Detail & Related papers  (2025-11-17T04:47:39Z)
• C3-Diff: Super-resolving Spatial Transcriptomics via Cross-modal Cross-content Contrastive Diffusion Modelling [5.986183062217602]
  This study presents a cross-modal cross-content contrastive diffusion framework, called C3-Diff, for ST enhancement with histology images as guidance.<n>To overcome the problem of low sequencing sensitivity in ST maps, we perform nosing-based information augmentation on the surface of feature unit hypersphere.<n>We propose a dynamic cross-modal imputation-based training strategy to mitigate ST data scarcity.
  arXiv  Detail & Related papers  (2025-11-04T13:12:25Z)
• PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction [9.420121324844066]
  We propose PH2ST, a prompt-guided hypergraph learning framework, to guide multi-scale histological representation learning for spatial gene expression prediction.<n> PH2ST not only outperforms existing state-of-the-art methods, but also shows strong potential for practical applications such as imputing missing spots, ST super-resolution, and local-to-global prediction.
  arXiv  Detail & Related papers  (2025-03-21T03:10:43Z)
• Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling [77.26556208024633]
  3D molecule generation is crucial for drug discovery and material science.<n>Existing approaches typically maintain separate latent spaces for invariant and equivariant modalities.<n>We propose textbfUAE-3D, a multi-modal VAE that compresses 3D molecules into latent sequences from a unified latent space.
  arXiv  Detail & Related papers  (2025-03-19T08:56:13Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [52.106879463828044]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• HSIGene: A Foundation Model For Hyperspectral Image Generation [46.745198868466545]
  Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. Due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. We propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution.
  arXiv  Detail & Related papers  (2024-09-19T05:17:44Z)
• Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics [5.020980014307814]
  spatial transcriptomics allows to characterize spatial gene expression within tissue for discovery research. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. This paper proposes a cross-modal conditional diffusion model for super-resolving ST maps with the guidance of histology images.
  arXiv  Detail & Related papers  (2024-04-19T16:01:00Z)
• Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features [0.0]
  TRIPLEX is a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs) Our benchmark study, conducted on three public ST datasets, demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC) The model's predictions align closely with ground truth gene expression profiles and tumor annotations, underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.
  arXiv  Detail & Related papers  (2024-03-12T12:25:38Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Geometric Latent Diffusion Models for 3D Molecule Generation [172.15028281732737]
  Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries. We propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM)
  arXiv  Detail & Related papers  (2023-05-02T01:07:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.