Yoichiro Yamamoto
Yoichiro Yamamoto researches machine learning for medical images, linking tissue patterns with disease information and possible treatment decisions. Yamamoto coauthored research on diagnosing breast tumors through the shapes of surrounding myoepithelial cells, using machine learning. The study connected measurable tissue features with a medical classification problem. Yamamoto directs RIKEN’s Biomedical Spatial Science Team. The team connects mathematical modeling, machine learning and life sciences to study disease mechanisms, therapies and individualized treatment.
Timeline
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2017
Learning from breast-tumor tissue
Yamamoto coauthored research on diagnosing breast tumors through the shapes of surrounding myoepithelial cells, using machine learning. The study connected measurable tissue features with a medical classification problem.
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2019
Finding explainable tissue patterns
He was a named coauthor of work on acquiring explainable knowledge from unannotated histopathology images. Another shared paper studied deep learning and expert approaches for finding prostate-cancer clues in magnetic-resonance images.
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2023–2024
Studying pancreatic-cancer outcomes
He coauthored a 2023 study using histopathology images to predict pancreatic-cancer recurrence after surgery and a 2024 study of one-year prognosis using peritoneal washing cytology.
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As of October 2026
Biomedical spatial science
Yamamoto directs RIKEN’s Biomedical Spatial Science Team. The team connects mathematical modeling, machine learning and life sciences to study disease mechanisms, therapies and individualized treatment.
Sources and credits
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