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 (ViT)-based models are largely limited to local heatmaps and do not reveal how morphological concepts contribute to ST predictions. Here, we introduce an explainable framework that combines relevance propagation and concept discovery to link transcriptional programs to tissue morphology. We developed a ViT-based framework for virtual ST from H&E images that combines ViT-aware layer-wise relevance
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Amos Muench →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Jonathan Thielmann →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Reduan Achtibat →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Maximilian Dreyer →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Philip Bischoff →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Caroline Forsythe →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Hamidreza Parand →
“Concept-based explanation of gene expression prediction from H&E images”
- LinkedLinked via arxiv author · 85%Thomas Walter →
“Concept-based explanation of gene expression prediction from H&E images”