Yonsei University Health System (YUHS)
M.S. in Digital Analytics
shiwon1998(at)gmail.com
Education
Experience
I am an AI researcher with primary interests in developing scalable and data-efficient learning systems, particularly for applications in medical imaging. My work focuses on learning from evolving and limited (scarce or privacy-sensitive) data conditions that are common in real-world scenarios but remain challenging for deep learning models. Learn more about my research here.
Research keywords include: continual learning, few-shot learning, medical imaging, and so on.
I am currently a research assistant at the Medical Informatics Collaboration Unit (MCU) of Yonsei University Health System (YUHS) in Seoul, Republic of Korea. Previously, I finished my M.S. in Digital Analytics at Yonsei University in August 2025, under the supervision of Prof. Yu Rang Park at the Digital Healthcare Lab (DHLab). I received my B.B.A. in Business Administration from the same university in 2023. From August 2024 to February 2025, I was a visiting scholar at the CMU School of Computer Science as part of an Intensive AI Education Program sponsored by the South Korean government.
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Research Highlights
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
Shiwon Kim*, Dongjun Hwang*†, Sungwon Woo*, Rita Singh†
ICCV 2025 Workshop on Continual Learning in Computer Vision (CLVision)
[TL;DR] [Paper] [Poster] [Slides] [Video] [Code]
Challenged the assumption of limited access to prior data in few-shot class-incremental learning, and compared joint training with incremental learning to empirically assess the practical impact of full data access on model performance.
# continual learning # few-shot learning
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
Shiwon Kim*, Dongjun Hwang*†, Sungwon Woo*, Rita Singh†
ICCV 2025 Workshop on Continual Learning in Computer Vision (CLVision)
Challenged the assumption of limited access to prior data in few-shot class-incremental learning, and compared joint training with incremental learning to empirically assess the practical impact of full data access on model performance.
Classification Models for Arthropathy Grades of Multiple Joints Based on Hierarchical Continual Learning
Bong Kyung Jang*, Shiwon Kim*, Jae Yong Yu, JaeSeong Hong, Hee Woo Cho, Hong Seon Lee, Jiwoo Park, Jeesoo Woo, Young Han Lee†, Yu Rang Park†
La Radiologia Medica (IF 2024: 9.7)
[TL;DR] [Paper] [Slides] [Code]
Developed and validated a continual learning framework for arthropathy grade classification scalable across multiple joints, using hierarchically labeled radiographs of the knee, elbow, ankle, shoulder, and hip from three tertiary hospitals.
# medical imaging # continual learning
Classification Models for Arthropathy Grades of Multiple Joints Based on Hierarchical Continual Learning
Bong Kyung Jang*, Shiwon Kim*, Jae Yong Yu, JaeSeong Hong, Hee Woo Cho, Hong Seon Lee, Jiwoo Park, Jeesoo Woo, Young Han Lee†, Yu Rang Park†
La Radiologia Medica(IF 2024: 9.7)
Developed and validated a continual learning framework for arthropathy grade classification scalable across multiple joints, using hierarchically labeled radiographs of the knee, elbow, ankle, shoulder, and hip from three tertiary hospitals.