Haruka Hirose — accepted AI-conference papers

2026

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

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

CVPR 2026

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

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2025

Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics

Kazuya Nishimura, Haruka Hirose, Ryoma Bise, Kaito Shiku, Yasuhiro Kojima

NeurIPS 2025poster

Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost. Point-wise loss functions have been widely used to minimize the discrepancy between predicted and absolute gene expression values. However, due to the complexity of the sequencing techniques and in…

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