RIKEN Center for Advanced Intelligence Project Data-Driven Biomedical Science Team
Team Leader: Ichiro Takeuchi (D.Eng.)
Research Summary

In the field of biomedical science, rapid advances in measurement technology allow us to collect a massive scientific dataset. An attempt aiming for a new scientific discovery based on such a massive scientific dataset is now realized as the fourth scientific paradigm followed by traditional three approaches based on theory, experiment, and simulation. Using artificial intelligence and machine learning techniques, we have a chance to find novel scientific hypotheses which are difficult to obtain only from knowledge and experiences of human experts. In our team, we study fundamental computational and mathematical techniques for data-driven scientific discovery, and demonstrate the effectiveness of these techniques in the field of biomedical science.
Main Research Fields
- Computer Science
Related Research Fields
- Materials Sciences
- Biology & Biochemistry
- Molecular Biology & Genetics
- Clinical Medicine
- Mathematics
Research Subjects
- Data Science
Selected Publications
- 1.
Shiraishi T., Miwa D., Katsuoka T., Duy V.N.L., Taji K., Takeuchi I.:
"Statistical Test for Attention Maps in Vision Transformers"
International Conference on Machine Learning (2024) - 2.
Duy V.N.L., Lin H.T., Takeuchi I.:
"CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference"
AI&Statistics (2024) - 3.
Goto K., Tamehiro N., Yoshida T., Hanada H., Sakuma T., Adachi R., Kondo K., Takeuchi I.:
"Novel Machine Learning Method AllerStat Identifies Statistically Significant Allergen-Specific Patterns in Protein Sequences."
Journal of Biological Chemistry. Vol.299-6, 104733 (2023) - 4.
Hashimoto N., Takagi Y., Masuda H., Miyoshi H., Kohno K., Nagaishi M., Sato K., Takeuchi M., Furuta T., Kawamoto K., Yamada K., Moritsubo M., Inoue K., Shimasaki Y., Ogura Y., Imamoto T., Mishina T., Tanaka K., Kawaguchi Y., Nakamura S., Ohshima K., Hontani H., Takeuchi I.:
"Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning."
Medical Image Analysis. Vol.85 (2023) - 5.
Kato H., Hanada H., Takeuchi I.:
"Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening."
IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol.45-2, pp.2330-2343 (2023) - 6.
Miwa D., Duy V.N.L., Takeuchi I.:
"Valid P-Value for Deep Learning-driven Salient Region"
The International Conference on Learning Representation (2023) - 7.
Ndiaye E., Takeuchi I.:
"Root-finding Approaches for Computing Conformal Prediction Set"
Machine Learning (2022) - 8.
Duy V.N.L., Takeuchi I.:
"More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming."
Journal of Machine Learning Research. Vol.23(300) (2022) - 9.
Duy V.N.L., Iwazaki S., Takeuchi I.:
"Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference."
Neural Information Processing Systems (2022) - 10.
Inatsu Y., Takeno S., Karasuyama M., Takeuchi I.:
"Bayesian Optimization for Distributionally Robust Chance-constrained Problem."
International Conference on Machine Learning (2022)
Related Links
Lab Members
Principal investigator
- Ichiro Takeuchi
- Team Leader
Core members
- Hiroyuki Hanada
- Research Scientist
- Noriaki Hashimoto
- Research Scientist
- Shigeyuki Matsui
- Senior Visiting Scientist
- Toru Ujihara
- Visiting Scientist
- Keiichi Inoue
- Visiting Scientist
- Vo Nguyen Le Duy
- Visiting Scientist
- Shion Takeno
- Visiting Scientist
- Kentaro Kutsukake
- Visiting Scientist
- Shuichi Nishino
- Junior Research Associate
- Xudong Chen
- Research Part-time Worker I
- Onur Boyar
- Research Part-time Worker I
Contact Information
#427 4th Floor Bldg. 2, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya, Aichi,
464-8603, Japan
Email: ichiro.takeuchi@riken.jp