Concept-based explanation of gene expression prediction from H&E images
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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: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.
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.
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:2610.17603v1 Announce Type: cross Abstract: Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly sup...
arXiv:2608. 14924v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression.