Advanced Image Processing Techniques Lab @ Yonsei

Principal Investigator: Prof. Joon Yul Choi, Ph.D.

Prof. Joon Yul Choi received his Ph.D. in Electrical and Computer Engineering from Seoul National University. He subsequently conducted research at the Cleveland Clinic and the City University of New York, where he focused on the integration of engineering and medicine. He is currently a faculty member in the Department of Biomedical Engineering at Yonsei University.

His research focuses on quantitatively analyzing structural and functional changes associated with disease using clinical data, and integrating these findings with artificial intelligence for applications in diagnosis, prognosis prediction, and risk assessment (E-mail: jychoi717@yonsei.ac.kr).

Professional Experience

Chair, Department of Biomedical Engineering, Yonsei University (2026 - Present)
Associate Professor, Department of Biomedical Engineering, Yonsei University, (2026 - Present)
Assistant Professor, Department of Biomedical Engineering, Yonsei University, (2023 - 2026)
Senior Research Associate, City University of New York (2022 - 2023)
Research Fellow, Cleveland Clinic (2019 - 2022)
Research Specialist, University of Pennsylvania (2013 - 2015)

Research Areas

Our lab investigates how subtle biological changes are reflected in medical imaging and how such information can contribute to clinical decision-making. To this end, we integrate advanced image processing techniques with artificial intelligence, with a strong emphasis on generalizability and clinical applicability across multi-institutional and multi-scanner environments.

Quantitative Medical Imaging and Biomarkers

A core focus of our lab is to move beyond treating medical imaging as a purely observational tool and instead derive reliable quantitative metrics that serve as imaging biomarkers. By quantifying microstructural and functional characteristics from medical images, we aim to achieve a more precise understanding of normal and pathological conditions. This approach enables the detection of subtle changes even in the absence of visible lesions, as well as in early or borderline stages of disease.

AI for Diagnosis, Prognosis and Risk Stratification

Our lab focuses on leveraging artificial intelligence as a predictive tool for clinical decision support. Using AI models based on medical imaging and biosignals, we aim to detect disease presence and predict prognosis, functional outcomes, and risk levels. In addition to model performance, we place strong emphasis on interpretability and employ large language models to translate AI predictions into clinically meaningful insights. This approach enhances both the interpretability and real-world applicability of AI outputs in clinical settings.

Multimodal and Real-World Clinical Data Analysis

Single-modality imaging is often insufficient to capture the complexity of clinical conditions. To address this limitation, our lab integrates multimodal data, including imaging, clinical information, and biosignals, across multiple institutions. Such data are inherently heterogeneous due to variations across sites and scanners, and ideal conditions are rarely achievable in real-world clinical settings. To address these challenges, we develop AI models with strong generalizability and reproducibility, enabling our findings to extend beyond specific datasets and translate effectively into clinical practice.

Lab Members

PhD Students

Cheol-Woon Kim

Strong resilience

Research Interests: Focuses on extracting quantitative biomarkers from diverse clinical populations for outcome prediction

E-mail: kimcheolwoon53@yonsei.ac.kr

Dohyeon Kim

I aspire to become a researcher who identifies clinically meaningful scientific evidence for real-world applications.

Research Interests: Focuses on analyzing cerebrovascular reactivity and functional connectivity using fMRI and investigating their associations with clinical measures

E-mail: kdh991222@yonsei.ac.kr

Jonghun Lee

Strong commitment to research

Research Interests: Focuses on quantifying qualitative information and addressing clinical questions using statistical and data-driven approaches

E-mail: mabubjh@yonsei.ac.kryonsei.ac.kr

Wonpil Jang

Motivated to always give my best, I aspire to pursue engaging and impactful research in biomedical engineering.

Research Interests: Focuses on longitudinal studies based on brain structural features such as volume, thickness, and surface area, as well as AI-driven analysis using MRI data

E-mail: xv0077@yonsei.ac.kr

Yechan Kim

I aim to develop research that can make a meaningful impact on real-world healthcare.

Research Interests: Focuses on analyzing structural connectivity in the brain and quantitatively evaluating interactions between cerebral blood flow and intrinsic neural activity

E-mail: dpcks546@yonsei.ac.kr

Dohyeong Kwon

With a steady and persistent approach, I strive to contribute to meaningful and positive change through research

Research Interests: Focuses on developing advanced analytical techniques for brain microstructure visualization, aiming to enhance data precision for early detection of subtle pathological changes

E-mail: dessinn01@yonsei.ac.kr

Seunghwan Han

Aims to create clinically meaningful impact in medical image analysis through deep learning-based approaches.

Research Interests: Focuses on developing deep learning-based automated pipelines for high-precision segmentation of microstructural features in medical imaging

E-mail: vet3377@yonsei.ac.kr

Master's Students

Junbeom Lee

Aims to contribute as a small but essential part of advancing real-world clinical practice through research
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Research Interests: Focuses on AI-based segmentation of key anatomical structures in medical imaging

E-mail: junbeom3871@yonsei.ac.kr

Henry David Mupati

I am a God-fearing individual with strong respect for others , passionate about advancing human health through AI-Driven biomedical solutions ㅤ

Research Interests: Focuses on the application of artificial intelligence in biomedical engineering, particularly in disease prediction and medical imaging

E-mail: hmupati@yonsei.ac.kr

Indika Yasan Don Hettiarachchige

I am an honest and calm person. I am motivated to expand my knowledge in Biomedical Engineering to further develop my career

Research Interests: Focuses on decision-making within Behavioral Economics and Economic Psychology, using statistical modeling approaches

E-mail: indikayasan.eng@yonsei.ac.kr

Wonjin Kim

Hello, I’m Wonjin Kim. I like to stay active, so my brain doesn’t have to do all the work

Research Interests: Focuses on predicting clinical outcomes from medical imaging using deep learning

E-mail: kimwj1001@yonsei.ac.kr

Research Assistants

Yubin Oh

A trainee researcher in the “Research Training Camp” ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ ㅤ

Research Interest: Focuses on analyzing the effects of clinical and environmental factors on brain structure and imaging-derived biomarkers ㅤ ㅤ ㅤ

E-mail: yubinoh48@yonsei.ac.kr

Jaeseung Kim

Driven by an iterative cycle of inquiry, transforming questions into experiments and experiments into new questions

Research Interest: Focuses on deep learning for brain imaging, particularly on advancing quantitative and interpretable MRI analysis

E-mail: xmflak20@gmail.com

Research Affiliates

Tae Keun Yoo, M.D.

BSM Eye Clinic,

Ophthalomology

E-mail: eyetaekeunyoo@gmail.com

Hyun Ho Kim, M.D.

Myongji Hospital,

Orthopedics Surgery

E-mail: mdryan0920@gmail.com

Young-jae Jang, Ph.D.

Seoul National University Hospital,

Radiation Oncology

E-mail: phyjyj@gmail.com

Selected Publication

Kim D, Kim JJ, Kim Y, Duncan D, Amyot F, Kenney K, Diaz-Arrastia RR, Choi JY*, Comparative evaluation of resting-state and CO2-induced cerebrovascular reactivity in patients with traumatic brain injury, Journal of Neurotrauma, 2026
Lee J, Kim Y, Lee J, Choi JY*, Lee W, Altered cortical myelination based on gray-to-white matter signal intensity contrast in shift workers, Brain Structure & Function, 230(9):179, 2025 (Top 2.3% in Anatomy & Morphology)
Jang W, Kim S, Kim YJ, Lee S, Choi JY*, Lee W, Overwork and changes of brain structure: A pilot study, Occupational & Environmental Medicine, 82(3):105-111, 2025 (BMJ Press Selected)
Choi JY*, Kim DE, Kim SJ, Choi H, Yoo TK, Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction, NPJ Digital Medicine, 8(1):82, 2025 (Top 0.3% in Health Care Sciences & Services)
Su T-Y, Choi JY*, Hu S, Wang X, Blümcke I, Chiprean K, Krishnan B, Ding Z, Sakaie K, Murakami H, Alexopoulos AV, Najm I Jones SE, Ma D, Wang ZI, Multiparametric characterization of focal cortical dysplasia using 3D MR fingerprinting, Annals of Neurology, 96(5):944-957, 2024 (Cover Article Selected)