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Yasuhiro Kojima

3 accepted papers

2026

Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection

AAAI 2026technical

Spatial transcriptomics (ST) is a novel technology that enables the observation of gene expression at the resolution of individual spots within pathological tissues. ST quantifies the expression of tens of thousands of genes in a tissue section; however, heavy observational noise is often introduced

Cited by 0SourcePDFScholar
2026

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

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

Cited by 0SourcecodeScholar
2025

Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics

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…

Cited by 0SourcecodeScholar