8 citations · 17 across the 18 of their papers we have counts for
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Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models
Niccolò Fineschi, Jovana Panic, Atsushi Watanabe +7
Retinal foundation models have demonstrated strong performance across a wide range of ophthalmic tasks, yet relatively little is known about how disease-related information is repr…
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…
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 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,…
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…