8 papers · 1 filter
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise +4
Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing…
Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
Kazuya Nishimura, Ryoma Bise, Haruka Hirose +1
Deep learning-based nuclei segmentation and classification in pathology images typically rely on large-scale pixel-level manual annotations, which are costly and difficult to obtai…
FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation
Yukinori Yamamoto, Kazuya Nishimura, Tsukasa Fukusato +3
Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privacy constraints and the high cost…
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…
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…