1 citations · 2 across the 20 of their papers we have counts for
15 papers · 1 filter
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…
Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning
Shiku Kaito, Shinnosuke Matsuo, Daiki Suehiro +1
The paper proposes a novel multi-class Multiple-Instance Learning (MIL) problem called Learning from Majority Label (LML). In LML, the majority class of instances in a bag is assig…
NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models
Shumpei Takezaki, Ryoma Bise, Shinnosuke Matsuo
In this study, we propose a novel data augmentation method that introduces the concept of CutMix into the generation process of diffusion models, thereby exploiting both the abilit…
Computer-Aided Multi-Stroke Character Simplification by Stroke Removal
Ryo Ishiyama, Shinnosuke Matsuo, Seiichi Uchida
Multi-stroke characters in scripts such as Chinese and Japanese can be highly complex, posing significant challenges for both native speakers and, especially, non-native learners.…
Instance-wise Supervision-level Optimization in Active Learning
Shinnosuke Matsuo, Riku Togashi, Ryoma Bise +2
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness c…
Learning from Partial Label Proportions for Whole Slide Image Segmentation
Shinnosuke Matsuo, Daiki Suehiro, Seiichi Uchida +4
In this paper, we address the segmentation of tumor subtypes in whole slide images (WSI) by utilizing incomplete label proportions. Specifically, we utilize `partial' label proport…
Counting Network for Learning from Majority Label
Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro +1
The paper proposes a novel problem in multi-class Multiple-Instance Learning (MIL) called Learning from the Majority Label (LML). In LML, the majority class of instances in a bag i…
Deep Attentive Time Warping
Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan +4
Similarity measures for time series are important problems for time series classification. To handle the nonlinear time distortions, Dynamic Time Warping (DTW) has been widely used…
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions
Takanori Asanomi, Shinnosuke Matsuo, Daiki Suehiro +1
Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are gi…
Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization
Shinnosuke Matsuo, Ryoma Bise, Seiichi Uchida +1
This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instanc…
Self-Augmented Multi-Modal Feature Embedding
Shinnosuke Matsuo, Seiichi Uchida, Brian Kenji Iwana
Oftentimes, patterns can be represented through different modalities. For example, leaf data can be in the form of images or contours. Handwritten characters can also be either onl…
Attention to Warp: Deep Metric Learning for Multivariate Time Series
Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan +4
Deep time series metric learning is challenging due to the difficult trade-off between temporal invariance to nonlinear distortion and discriminative power in identifying non-match…