arXiv AI
VGL-stGen: Learning a shared multimodal model for lung histology and spatial transcriptomics
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VGL-stGen: Learning a shared multimodal model for lung histology and spatial transcriptomics
VGL-stGen is a multimodal large language model adapted from a pretrained medical vision-language model using paired lung H&E and spatial transcriptomics with single-cell references. Training integrate...
arXiv Computer Vision
Oct 21
REST: Reliability-Guided Spatial Transcriptomics Supervision for Gene Expression Prediction from Histopathological HE-Stained Images
arXiv:2610.26988v1 Announce Type: new Abstract: Histopathology is central to cancer diagnosis, but morphology in Hematoxylin and Eosin (HE)-stained images does not directly reveal the molecular state...
arXiv AI
Aug 18
Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics
arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).
arXiv Machine Learning
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Biology-Guided Context-Aware Residual Propagation for Spatial Gene Expression Prediction from Histology
The paper introduces BCARP, a Biology‑Guided Context‑Aware Residual Propagation framework that predicts spatial gene expression from H&E‑stained histology images. Rather than directly decoding expression from visual features, BCARP treats histology embeddings as morphological priors and iteratively refines spot representations in a transcriptomics‑oriented latent space, integrating nuclei‑guided local context and prototype‑guided global propagation. Evaluations on three spatial transcriptomics datasets and ten HEST‑1k cohorts show that BCARP outperforms existing baselines in mean correlation‑based metrics, demonstrating the effectiveness of biology‑guided local‑global residual propagation for histology‑to‑transcriptomics prediction.
arXiv Computer Vision
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Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images
arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
Hugging Face Trending Papers
Aug 17
Concept-based explanation of gene expression prediction from H&E images
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods for vision transformer...