arXiv Machine Learning
Bridging the Modality Bottleneck in Pathology MIL through Molecularly Guided Feature Adaptation
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arXiv Machine Learning
Jun 3
Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model
arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.
arXiv AI
Jun 30
Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction
arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
arXiv Machine Learning
Oct 28
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.
Hugging Face Trending Papers
Oct 13
MIST: Multi-Scale Spatial Context for Gene Expression Prediction from Histology
MIST is a Multi‑context Spatial Transformer that predicts spatial gene expression from H&E images by adding two context pathways to a standard Transformer: a coordinate‑invariant local pathway that attends to spatial k‑nearest‑neighbor spots, and a slide pathway that broadcasts a whole‑slide summary to every spot. In intra‑cohort evaluation across six human organ cohorts, MIST improves mean Pearson correlation by 9.7%–11.6% over the strongest baseline for 10‑, 50‑, and 100‑gene panels, and achieves lower MSE and MAE. It also outperforms baselines in leave‑one‑organ‑out transfer, domain recovery, and reproduces spatial expression patterns of cancer‑marker genes.
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...
arXiv Computer Vision
Sep 30
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...