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. We show that training a lightweight alignment module atop frozen histopathology and RNA-Seq foundation models enables open-vocabulary molecular prompting -- querying H&E slides with gene-set signatures to predict pathway activity without sequencing or end-to-end retraining. Using contrastive learning on a multi-cancer c
Advances in foundation models for both histopathology and RNA-Seq are mature enough to facilitate cross-modal alignment, making data-efficient molecular prediction possible.
This development significantly lowers the cost and increases the accessibility of high-resolution molecular diagnostics, potentially democratizing oncology research and personalized medicine.
Molecular predictions that previously required expensive and time-consuming RNA sequencing can now be inferred from widely available H&E slides using a lightweight AI module.
- · Biopharmaceutical companies
- · Oncology researchers
- · Pathology labs
- · AI in healthcare developers
- · Traditional high-cost molecular sequencing service providers
Accelerated discovery of new therapeutic targets and biomarkers for various cancers.
Broader adoption of AI-driven diagnostic tools in routine clinical pathology reduces diagnostic turnaround times and costs.
Enhanced stratification of patients for clinical trials and personalized treatment, leading to improved patient outcomes and more efficient drug development.
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Read at arXiv cs.AI