CVPR 2026

Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images

Kazuya Nishimura, Ryoma Bise, Shinnosuke Matsuo, Haruka Hirose, Yasuhiro Kojima

Abstract

Estimating slide- and patch-level gene expression profiles from pathology images enables rapid and low-cost molecular analysis with broad clinical impact. Despite strong results, existing approaches treat gene expression as a mere slide- or spot-level signal and do not incorporate the fact that the measured expression arises from the aggregation of underlying cell-level expression. To explicitly introduce this missing cell-resolved guidance, we propose a Cell-type Prototype-informed Neural Network (CPNN) that leverages publicly available single-cell RNA-sequencing datasets. Since single-cell measurements are noisy and not paired with histology images, we first estimate cell-type prototypes — mean expression profiles that capture stable gene–gene co-variation patterns. CPNN then learns cell-type compositional weights directly from images and models the relationship between prototypes and observed bulk or spatial expression, providing a biologically grounded and structurally regularized prediction framework. We evaluate CPNN on three slide-level datasets and three patch-level spatial transcriptomics datasets. Across all settings, CPNN achieves the highest performance in terms of Spearman correlation. Moreover, by visualizing the inferred compositional weights, our framework provides interpretable insights into which cell types drive the predicted expression.

Overview

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Figure 1: Overview of the proposed CPNN method.

Results

CPNN is evaluated on three slide-level bulk transcriptomics datasets and three patch-level spatial transcriptomics datasets. Across all settings, CPNN achieves the highest performance in terms of Spearman correlation compared to existing methods. Visualization of inferred cell-type compositional weights further provides interpretable, biologically meaningful insights.

Citation

@inproceedings{nishimura2026cpnn,
  title     = {Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images},
  author    = {Nishimura, Kazuya and Bise, Ryoma and Matsuo, Shinnosuke and Hirose, Haruka and Kojima, Yasuhiro},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026},
}