Ashley P. Tsang

Frozen pathology foundation-model embeddings transfer poorly to single-cell spatial gene expression prediction from H&E: on the 5K gene panel, mean per-gene Pearson correlation falls from 0.375 at pseudo-spot resolution to 0.065 at single-cell resolution. A set of single-cell-oriented lightweight adaptations, namely cell-localized token pooling, 40x field-of-view inputs, Poisson count supervision and LoRA fine-tuning, raises the same panel to 0.174 and beats SpatialEx on the 5K panel with AUROC 0.800 versus 0.642. Gains concentrate in spatially variable and disease-relevant genes, while absolute correlation across the full panel stays low.

Frozen pathology foundation-model embeddings transfer poorly to single-cell spatial gene expression prediction from H&E: on the 5K gene panel, mean per-gene Pearson correlation falls from 0.375 at pseudo-spot resolution to 0.065 at single-cell resolution. A set of single-cell-oriented lightweight adaptations, namely cell-localized token pooling, 40x field-of-view inputs, Poisson count supervision and LoRA fine-tuning, raises the same panel to 0.174 and beats SpatialEx on the 5K panel with AUROC 0.800 versus 0.642. Gains concentrate in spatially variable and disease-relevant genes, while absolute correlation across the full panel stays low.