Faculty Research Profile
Jimin Lee
학력
• 2021: Ph. D. Biomedical Radiation Sciences, Seoul National University
• 2015: B. S. Nuclear & Quantum Engineering, KAIST
주요 경력
• 2025.09 – Present: Associate Professor, UNIST
• 2021.03 – 2025.08: Assistant Professor, UNIST
• 2015: Intern, Australian Nuclear Science and Technology Organization (ANSTO)
수상/학회/외부활동
• 2021: Best Paper Award from Seoul National University
• 2016: Best Poster Award at 2016 Korean Nuclear Society Spring Meeting
• 2017 – Present: Executive of TensorFlow Korea
• 2016 – Present: Member of American Association of Physicists in Medicine
• 2016 – Present: Member of Korean Nuclear Society
방사선 및 의료 지능 연구실
Radiation & Medical Intelligence Lab
방사선 및 의료 지능 연구실은 의료, 방사선, 원자력에 대한 기반 지식을 바탕으로 인공지능 기술을 접목하는 다양한 연구를 수행하고 있습니다. 주로 의료영상 및 여러 바이오메디컬 데이터에 컴퓨터 비전 기술을 적용하고 있으며, 컴퓨터 단층영상에서의 인공음영 제거 기술 개발, 주요 장기 자동분할 기술 개발 등 임상에 실질적으로 도움이 되는 기술 개발을 목표로 합니다. 이를 위하여 국내외 다수 대학병원 및 여러 협력기업들과 공동 연구를 진행하고 있습니다.
Radiation & Medical Intelligence Lab (RAMI Lab) has conducted various research applying artificial intelligence (AI) technology into medical, radiation, and nuclear engineering domain. The research has been mainly focused on computer vision tasks such as classification, segmentation, image translation and generation. We aim to develop various AI technologies, which would be practically helpful in clinical practice. Currently, we are conducting collaborative research with numerous university hospitals and partner companies both domestically and internationally.
Radiation & Medical Intelligence Lab (RAMI Lab) has conducted various research applying artificial intelligence (AI) technology into medical, radiation, and nuclear engineering domain. The research has been mainly focused on computer vision tasks such as classification, segmentation, image translation and generation. We aim to develop various AI technologies, which would be practically helpful in clinical practice. Currently, we are conducting collaborative research with numerous university hospitals and partner companies both domestically and internationally.
연구분야
Physics-informed Artificial Intelligence (AI), Computer Vision, Medical Imaging (X-ray, CT), Radiation Physics
Physics-informed Artificial Intelligence (AI), Computer Vision, Medical Imaging (X-ray, CT), Radiation Physics
연구주제
• 도메인 지식을 기반으로 한 인공지능 적용 기술 개발 / Physics-informed Artificial Intelligence
• 의료영상 기반 분류 및 주요 부위 분할 기술 개발 / Classification and Segmentation Technologies for Medical Images
• 의료영상 변환 및 생성 기술 개발 / Image-to-Image Translation and Generation Technologies for Medical Images
• Physics-informed Artificial Intelligence
• Classification and Segmentation Technologies for Medical Images
• Image-to-Image Translation and Generation Technologies for Medical Images
국가연구개발사업 기술 분류체계
국가과학기술표준분류
EG. 원자력 > EG07. 방사선기술 > EG0704. 방사선 의학/의공학 기술
논문
• Journal of Computational Physics / FPL-net: A deep learning framework for solving the nonlinear Fokker–Planck–Landau collision operator for anisotropic temperature relaxation / Hyeongjun Noh, Jimin Lee, Eisung Yoon / 2025 (co-corresponding)
• Nuclear Instruments and Methods in Physics Research Section A / Deep learning-based restoration of noise-corrupted and saturated beam profiles for real-time proton beam monitoring and quality assurance / Gwang-il Jung, Young Seok Hwang, Yu Mi Kim, Chan Young Lee, Jun Mok Ha, Eun Joo Oh, Jae Hyun Lee, Jimin Lee / 2025
• Scientific Reports / Dual-encoder architecture for metal artifact reduction for kV-cone-beam CT images in head and neck cancer radiotherapy / Juhyeong Ki, Jung Mok Lee, Wonjin Lee, Jin Ho Kim, Hyeongmin Jin, Seongmoon Jung, Jimin Lee / 2024
특허
• Apparatus and method for diagnosing patent ductus arteriosus in newborns based on deep learning, Korea, 2024 (10-2024-0095482)
• Device for translating magnetic resonance imaging to computed tomography image using deep learning and method thereof, Korea, 2024 (10-2024-0093703)