6 papers
Preserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression Prediction
Kaito Shiku, Kazuya Nishimura, Yasuhiro Kojima +1
Predicting spatial gene expression from histology images could scale spatial transcriptomics (ST) to image-only cohorts, but conventional histology-based ST prediction is trained a…
Leveraging Cell-level Spatial Transcriptomics as Weak Supervision for Nuclei Classification
Kazuya Nishimura, Ryoma Bise, Haruka Hirose +1
Deep learning-based nuclei analysis in histopathology relies heavily on large-scale, expert-curated datasets that are costly and difficult to scale across organs. To address this,…
Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images
Kazuya Nishimura, Ryoma Bise, Shinnosuke Matsuo +2
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, e…
Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
Kazuya Nishimura, Haruka Hirose, Ryoma Bise +2
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 be…
Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
Kaito Shiku, Kazuya Nishimura, Shinnosuke Matsuo +2
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…
Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
Kazuya Nishimura, Ryoma Bise, Yasuhiro Kojima
Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely relat…