Kaito Shiku
Kaito Shiku
志久 開人
PhD Student · Kyushu University
I am currently a second-year PhD student at the Graduate School of Information Science and Electrical Engineering , supervised by Prof. Ryoma Bise .

I am also a member of the JST ASPIRE Project (Medical × AI) and currently visiting DFKI (German Research Center for Artificial Intelligence) as a visiting researcher.
Expertise
Computer Vision / Medical Image Analysis / Label-Efficient Learning

Research Interests

My research focuses on label-efficient machine learning for medical image analysis. I have worked on a variety of medical imaging modalities, including cell culture images, endoscopic images, computed tomography (CT) images, and histopathological images. Recently, I have developed a strong interest in gene expression analysis, and my current research increasingly concentrates on this topic.

Medical Image Analysis Computer Vision Machine Learing Gene Expression Analysis

News

2026/05 Our paper Weakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels has been accepted in MICCAI workshop AMAI 2026 @ Strasbourg.
Nao Sugeta, Kaito Shiku, Shinnosuke Matsuo and Ryoma Bise
2026/05 Our paper Hierarchical Co-Embedding of Font Shapes and Impression Tags has been accepted in ICDAR 2026 @ Vienna (Top-tier conference in document image analysis).
Yugo Kubota, Kaito Shiku and Seiichi Uchida
2026/05 Our paper Leveraging Vision-Language Models as Weak Annotators in Active Learning has been accepted in ICIP 2026 @ Tampere (International conference on image processing).
Phuong Ngoc Nguyen, Kaito Shiku, Ryoma Bise, Seiichi Uchida and Shinnosuke Matsuo
2026/03 Awarded the 2025 Excellent Student Award by the IEEE 🎉 Fukuoka Section.
2026/01 Our paper Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking has been accepted in ISBI 2026 @ London (International conference on medical image analysis; Oral).
Kaito Shiku, Ichika Seo, Tetsuya Matoba, Rissei Hino, Yasuhiro Nakano and Ryoma Bise
2025/11 Our paper Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection has been accepted in AAAI 2026 @ Singapore 🎉 (Top-tier conference in AI, CORE Rank A*).
Kaito Shiku, Kazuya Nishimura, Shinnosuke Matsuo, Yasuhiro Kojima, and Ryoma Bise
2025/09 Our paper Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics has been accepted in NeurIPS 2025 @ San Diego 🎉 (Top-tier conference in machine learning, CORE Rank A*).
Kazuya Nishimura, Haruka Hirose, Ryoma Bise, Kaito Shiku and Yasuhiro Kojima
2025/08 Our paper Single-cell Synaptome Mapping of Endogenous Protein Subpopulations in Mammalian Brain has been accepted in Nature Communications 🎉 (Impact factor = 15.7).
Motokazu Uchigashima, Risa Iguchi, Kazuma Fujii, Kaito Shiku, Pratik Kumar, Xinyi Liu, Mari Isogai, Chiaki Hoshino, Manabu Abe, Motohiro Nozumi, Yosuke Okamura, Michihiro Igarashi, Kenji Sakimura, Ryoma Bise, Luke D Lavis and Takayasu Mikuni
2025/08 Our paper Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning has been accepted in Pattern Recognition (Impact factor = 7.6).
Shiku Kaito, Shinnosuke Matsuo, Daiki Suehiro and Ryoma Bise
2025/08 Our paper Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses has been accepted in Workshop on MICCAI 2025 @ Korea (International conference on medical image analysis).
Takamasa Yamaguchi, Brian Kenji Iwana, Ryoma Bise, Shota Harada, Takumi Okuo, Kiyohito Tanaka and Shiku Kaito
2024/08 Our paper Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer has been accepted in WACV 2025 @ Arizona (International conference on computer vision, CORE Rank A).
Shiku Kaito, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka and Ryoma Bise
2024/06 Accepted into the 次世代AI人材育成プログラム (K-BOOST) .
2023/12 Our paper Counting Network for Learning from Majority Label has been accepted in ICASSP 2024 @ Korea (Top-tier conference in signal processing, h-index=123).
Shiku Kaito, Shinnosuke Matsuo, Daiki Suehiro and Ryoma Bise
2023/07 Our paper Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection has been accepted in EMBC 2023 @ Sydney .
Shiku Kaito, Hiromitsu Shirai, Takeshi Ishihara and Ryoma Bise

Reviewing Experience

CVPR, NeurIPS, MICCAI, Pattern Recognition, MIRU

Contact

Address Kyushu University, Fukuoka, Japan