Our paper received the Certificate of Merit (Science) at RSNA 2025!

RSNA is a top conference in the clinical field of medical imaging with a very narrow acceptance rate of about 25% and attracts top radiologists and medical imaging researchers from all over the world.
At this year’ Read more...

Prof. Suzuki will have a panel discussion with Prof. Curtis P. Langlotz at Stanford University (President of RSNA) and Prof. Pranav Rajpurkar at Harvard University, along with Dr. Mindy Yang as a moderator.
The topic title is “Optimizing radiologist-AI collaboration in practice” Read more...

Our study has received an Encouragement Award.
This study, presented by Dr. Jin at the 44th JAMIT Annual Meeting (JAMIT 2025),was conducted by a master’s student who graduated in September 2024.

Xu Y., Jin Z., Sudo T., Watanabe H., and Suzuki K.: Development of a Radiation Dose Red Read more...

Professor Kenji Suzuki has been named on the World’s Top 2% Scientists list, which includes the most-cited scientists across the length of their career. This ranking is compiled by Stanford University, based on data from Elsevier’s Scopus. Prof. Suzuki has been ranked in both the “s Read more...

Prof. Suzuki was invited to give a talk with MICCAI distinguished speakers at “KAIST Day,” a kickoff event for MICCAI 2025. Read more...

Seven papers by Mustain Billah, Hanhong He, Jifeng Zu, Fatma Beltaief, Haitian Zhang (alumni), Shogo Kodera (alumni), and Dichao Liu (Researcher) have been accepted by 111th Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA 2025), known as the top clinical conf Read more...

A paper by Yuqiao Yang, a doctoral student in Suzuki Laboratory, has been published in IEEE Access.
Conglaturations!

Yang Y., Sato M., Jin Z., and Suzuki K.: Patch-based Deep-learning Model with Limited Training Dataset for Liver Tumor Segmentation in Contrast-enhanced Hepatic Comput Read more...

Dr. Suzuki has been awarded a research grant as a Principal Investigator for a national research project from JST K Program by Japan Science and Technology Agency (JST). Read more...

Our research accepted by IEEE Access with Yuqiao Yang as first author was introduced in EurekAlert!

MHP-Net: A revolutionary AI model for accurate liver tumor segmentation for diagnosis and Read more...

Our research presented by Shogo Kodera at RSNA 2024 and awarded the Cum Laud Award was featured in “Feature & Article” in EurekAlert!

Cancer diagnosis on your laptop? New a Read more...