19 papers
Weakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels
Nao Sugeta, Kaito Shiku, Shinnosuke Matsuo +1
In prostate cancer histopathology, the Gleason Score is determined by the most frequent (Primary) and second most frequent (Secondary) Gleason patterns within a whole-slide image.…
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 Vision-Language Models as Weak Annotators in Active Learning
Phuong Ngoc Nguyen, Kaito Shiku, Ryoma Bise +2
Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work, we investigate how vision-…
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…
Cell Instance Segmentation via Multi-Task Image-to-Image Schrödinger Bridge
Hayato Inoue, Shota Harada, Shumpei Takezaki +1
Existing cell instance segmentation pipelines typically combine deterministic predictions with post-processing, which imposes limited explicit constraints on the global structure o…
Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification
Shota Harada, Ryoma Bise, Kiyohito Tanaka +1
Semi-supervised domain adaptation leverages a few labeled and many unlabeled target samples, making it promising for addressing domain shifts in medical image analysis. However, ex…