1 citations · 2 across the 20 of their papers we have counts for
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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.…
Active Reference Acquisition in Few-Shot Font Generation
Shinnosuke Matsuo
Few-shot font generation aims to synthesize the remaining glyphs of a font given one or a few reference glyphs while preserving stylistic consistency, thereby supporting font desig…
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-…
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…
Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning
Kohki Akiba, Shinnosuke Matsuo, Shota Harada +1
Semi-supervised learning (SSL) often suffers under class imbalance, where pseudo-labeling amplifies majority bias and suppresses minority performance. We address this issue with a…