Pediatric Brain Age Prediction via Age-Gated Interpretable Dual-Pathway Model with Disentangled Myelination and Gyrification
ISMRM 2026
Oral Presentation
About Me
I am Changmin Ryu, an Integrated M.S./Ph.D. student in Electrical & Electronic Engineering at Yonsei University, and a researcher at the Medical Imaging Artificial Intelligence Laboratory.
My primary interests are Medical Image Processing, eXplainable AI, Clinical Applications, and Multi-Modality data analysis.
Open to Collaborations
Latest News
2026
Two papers accepted to ISMRM 2026, both selected as Oral Presentations.
2025
Received Best Trainee Scientific Awards Oral Presentation (Silver) at ICMRI 2025.
2025
Received KCR 2025 Best Abstract Awards.
2024
Received Best Trainee Scientific Awards Poster Presentation (Gold) at ICMRI 2024.
Research Experience
2025.03 - Present
Medical Imaging Artificial Intelligence Lab
Integrated M.S./Ph.D. student
2024.06 - 2025.02
Yonsei University (연세대학교), Seoul, Korea
Research Internship
MILAB (Medical Imaging Artificial Intelligence LABoratory)
2023.12 - 2024.06
The Catholic University of Korea St.Mary's hospital, Seoul, Korea
Research Internship
Spine stenosis detection
2023.06 - 2024.06
Hankuk University of Foreign Studies (한국외국어대학교)
Research Internship
HUFS AIMLAB (Augmented Intelligence Medical Imaging Laboratory)
Publications & Patents
Finite-difference based MR Electrical Properties Reconstruction Optimization Method at 3T
ISMRM 2026
Oral Presentation
Gyrification-Sensitive Brain Age Prediction in Neonates and Infants with Multi-Contrast Brain MRI Using Attention-Guided Deep Learning
ICMRI 2025
Oral / Silver Award
Attention-Guided Deep Learning Model for Predicting Myelination Age from Multi-contrast Brain MRI of Neonates and Infants
KCR 2025
Oral / Best Abstract Award
Infant Brain Age Estimation Using Deep Learning on Myelin-Sensitive T1w/T2w Ratio Images
KCR 2025
Oral Presentation
Attention-guided deep learning model focusing on myelination for predicting brain age using multi contrast MRI
ISMRM 2025
Oral Presentation
Improving subcortical segmentation in brain MRI using knowledge distillation to enhance robustness against motion artifacts
ISMRM 2025
Digital Poster
Improving subcortical segmentation in brain MRI using knowledge distillation to enhance robustness against motion artifacts
ICMRI 2024
Printed Poster / Gold Award
On the feasibility of detecting spinal stenosis using deep learning in radiography
KCR 2024
Oral Presentation
Quantitative analysis of MRI-visible perivascular spaces in schizophrenia
OHBM 2024
Printed Poster
Quantitative analysis of MRI-visible perivascular spaces in schizophrenia
ISMRM 2024
Printed Poster
Improved basal ganglia segmentation of three dimensional neonatal brain MR images for perivascular space assessment
ICMRI 2023
Oral Presentation
Zero-Shot Deep Anti-Aliasing Prior for Residual Artifact Suppression in non-Cartesian k-space MRI
IEEE Transactions on Biomedical Engineering
"영유아 뇌 나이 예측 방법 및 장치"
특허 출원
출원번호 10-2025-0195481
출원일자: 2025.12.10
"영유아 뇌 나이 추정 방법 및 장치"
특허 출원
출원번호 10-2025-0067307
출원일자: 2025.05.23
Academic Activities
Links
Awards & Honors
Best Trainee Scientific Awards Oral Presentation (Silver), ICMRI 2025
2025
KCR 2025 Best Abstract Awards
2025
Best Trainee Scientific Awards Poster Presentation (Gold), ICMRI 2024
2024