Abstract
We investigate whether modeling cellular context within transcriptomic
spots improves cell-based prediction of spatial gene expression
from histology images. Existing cell-based approaches independently
map each cell embedding to a gene score and therefore do not account for
relationships among nuclei within the same spot. We introduce a Contextual
Cell Transformer that jointly processes the cell embeddings in
each spot and averages the resulting context-dependent gene scores to
predict spot-level expression. We compare the proposed predictor with
non-contextual linear and multilayer perceptron predictors under identical
cell embeddings, target genes, aggregation, and optimization settings.
Experiments on the HER2ST and ccRCC datasets show that the
proposed predictor achieves lower mean squared error and higher mean
Pearson and Spearman correlations than both non-contextual cell-based
predictors. It also achieves the lowest mean squared error and highest
mean Pearson correlation among the compared patch- and cell-based
methods on both datasets, although the highest Spearman correlation
on each dataset is obtained by a patch-based baseline. These results
provide a proof-of-concept that intra-spot cellular context can improve
cell-based histology-to-spatial transcriptomics prediction.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/COMPAYL_014.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
BibTex
@InProceedings{KogRyo_Modeling_MICCAISAT2026,
author = { Koga, Ryoichi},
title = { { Modeling Intra-spot Cellular Context for Histology-to-Spatial Transcriptomics Prediction } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17251},
month = {pending},
page = {pending}
}