Prof . Dr . Dongheon Lee | Medical Image Analysis | Best Researcher Award

Assistant Professor at Seoul National University / College of Medicine, South Korea

Dr. Dongheon Lee is an Assistant Professor in the Department of Radiology at Seoul National University College of Medicine, with a joint appointment in the Interdisciplinary Program in Bioengineering. He specializes in medical image analysis, deep learning, and computer vision, with a strong emphasis on clinically relevant AI systems. His academic journey is deeply rooted in bioengineering, having completed both his M.S. and Ph.D. at Seoul National University. Dr. Lee has a proven record of innovation, evidenced by multiple high-impact publications and patents, many of which contribute directly to enhancing diagnostic accuracy and clinical workflow. He has served in leadership roles, including Deputy Director at Chungnam National University’s research institutes and active committee memberships. His research has received several national and international accolades, demonstrating both depth and translational impact. He continues to drive forward advancements at the intersection of AI and medical practice, with a focus on diagnostic technologies and clinical decision support.

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Education🎓 

Dr. Dongheon Lee’s educational foundation is built on interdisciplinary expertise in bioengineering and medical imaging. He earned his Ph.D. in Bioengineering from Seoul National University in 2020, under the mentorship of Professor Hee Chan Kim. His doctoral research, titled “Deep Learning Approaches for Clinical Performance Improvement: Applications to Colonoscopic Diagnosis and Robotic Surgical Skill Assessment”, reflects his early focus on practical, AI-based clinical solutions. Prior to that, he completed his M.S. in the same interdisciplinary program at Seoul National University in 2015, where he also concentrated on medical image analysis. His academic journey began with a B.S. degree in Electronic System Engineering from Hanyang University in 2013, providing him with a strong technical foundation in systems engineering and computational methods. This combination of engineering, medicine, and AI has shaped his approach to research and allowed him to work at the intersection of technology and clinical application with considerable effectiveness.

Professional Experience📝

Dr. Dongheon Lee has held multiple academic and research roles that showcase a steady progression in responsibility and impact. He currently serves as an Assistant Professor in the Department of Radiology at Seoul National University College of Medicine. Prior to this, he was an Assistant Professor in the Department of Biomedical Engineering at Chungnam National University from 2021 to 2025. During that time, he also served as Deputy Director at both the Biomedical Engineering Research Institute and the Big Data Center at Chungnam National University Hospital. Earlier in his career, Dr. Lee worked as a Research Assistant Professor and Research Specialist at the Biomedical Research Institute of Seoul National University Hospital. These roles have enabled him to gain comprehensive experience across clinical, academic, and data-intensive research environments. His career reflects a sustained commitment to developing AI solutions for healthcare, combining technical skill with clinical relevance in both research and educational settings.

Research Interest🔎

Dr. Dongheon Lee’s research interests lie at the intersection of medical image analysis, artificial intelligence, and computer vision, with a strong focus on clinical application. He is particularly invested in developing deep learning frameworks for diagnostic accuracy, disease classification, and surgical skill assessment. His work addresses real-world challenges in radiology and endoscopy, such as colorectal polyp detection and lung cancer screening, through robust AI-driven solutions. Dr. Lee is also deeply interested in uncertainty quantification, out-of-distribution detection, and the interpretability of AI models in clinical workflows. His research aims to make AI not only accurate but also explainable and trustworthy in medical environments. By integrating multimodal data and advanced visualization techniques, he seeks to improve human-AI collaboration in diagnosis and treatment planning. His ongoing projects involve 3D anatomical modeling, radiograph-based biological age estimation, and virtual simulation technologies, all of which reflect his mission to bridge engineering innovation with practical healthcare delivery.

Award and Honor🏆

Dr. Dongheon Lee has received multiple prestigious awards that underscore the impact and innovation of his research. In 2023, he was honored with the Medical Research Academic Award from Chungnam National University Hospital, recognizing his contributions to clinical imaging research. The same year, he was a winner in the MICCAI Grand Challenge (LDCTIQAC 2023), a significant achievement in the international medical image computing community. Earlier, in 2020, he received both the Outstanding Paper Award from Seoul National University Hospital and the ICT Colloquium Minister of Science and ICT Award, conferred by the Korean government and the Institute for Information & Communications Technology Planning & Evaluation (IITP). These awards highlight his excellence in both academic and applied domains, demonstrating a consistent ability to innovate in healthcare technologies. His achievements reflect strong peer recognition and align with his commitment to advancing artificial intelligence in real-world medical settings.

Research Skill🔬

Dr. Dongheon Lee possesses a robust and diverse set of research skills that bridge engineering, medical imaging, and artificial intelligence. He is highly proficient in deep learning model development for classification, detection, segmentation, and uncertainty estimation tasks, particularly in the context of radiological and endoscopic data. His expertise extends to algorithmic optimization, multi-modal data fusion, and computational modeling, with a focus on practical deployment in clinical workflows. Dr. Lee is experienced in designing and validating AI systems with real-world datasets, ensuring clinical relevance and regulatory compliance. He has also developed patented technologies for 3D anatomical mapping, lesion tracking, and endoscopic path guidance. Additionally, he demonstrates strong capabilities in interdisciplinary collaboration, leading cross-functional teams in bioengineering, computer science, and clinical departments. His skills in grant writing, manuscript preparation, and research leadership complement his technical acumen, enabling him to contribute meaningfully to both academic advancement and translational medical innovation.

Conclusion💡

Dr. Dongheon Lee is exceptionally qualified and stands out as a top-tier candidate for the Best Researcher Award. His research has made tangible impacts in clinical medicine, particularly through AI-driven diagnostics and medical imaging. The combination of high-impact publications, innovation through patents, and recognized academic leadership makes his profile exemplary.

With minor enhancements in global outreach and broader authorship representation, he could further solidify his stature as a global leader in biomedical AI.

Publications Top Noted✍

• Title: Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations
  Authors: EH Jin, D Lee, JH Bae, HY Kang, MS Kwak, JY Seo, JI Yang, SY Yang, …
  Year: 2020
  Citations: 137

• Title: Evaluation of surgical skills during robotic surgery by deep learning-based multiple surgical instrument tracking in training and actual operations
  Authors: D Lee, HW Yu, H Kwon, HJ Kong, KE Lee, HC Kim
  Year: 2020
  Citations: 104

• Title: CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates
  Authors: H Kim, D Lee, WS Cho, JC Lee, JM Goo, HC Kim, CM Park
  Year: 2020
  Citations: 49

• Title: Vision-based tracking system for augmented reality to localize recurrent laryngeal nerve during robotic thyroid surgery
  Authors: D Lee, HW Yu, S Kim, J Yoon, K Lee, YJ Chai, JY Choi, HJ Kong, KE Lee, …
  Year: 2020
  Citations: 29

• Title: Deep learning to optimize candidate selection for lung cancer CT screening: advancing the 2021 USPSTF recommendations
  Authors: JH Lee, D Lee, MT Lu, VK Raghu, CM Park, JM Goo, SH Choi, H Kim
  Year: 2022
  Citations: 28

• Title: Preliminary study on application of augmented reality visualization in robotic thyroid surgery
  Authors: D Lee, HJ Kong, D Kim, JW Yi, YJ Chai, KE Lee, HC Kim
  Year: 2018
  Citations: 27

• Title: Estimating maximal oxygen uptake from daily activity data measured by a watch-type fitness tracker: cross-sectional study
  Authors: SB Kwon, JW Ahn, SM Lee, J Lee, D Lee, J Hong, HC Kim, HJ Yoon
  Year: 2019
  Citations: 23

• Title: Augmented reality to localize individual organ in surgical procedure
  Authors: D Lee, JW Yi, J Hong, YJ Chai, HC Kim, HJ Kong
  Year: 2018
  Citations: 23

• Title: Online learning for the hyoid bone tracking during swallowing with neck movement adjustment using semantic segmentation
  Authors: D Lee, WH Lee, HG Seo, BM Oh, JC Lee, HC Kim
  Year: 2020
  Citations: 21

• Title: Endoscopists performance in optical diagnosis of colorectal polyps in artificial intelligence studies
  Authors: S Pecere, G Antonelli, M Dinis‐Ribeiro, Y Mori, C Hassan, L Fuccio, …
  Year: 2022
  Citations: 17

• Title: Low-dose computed tomography perceptual image quality assessment
  Authors: W Lee, F Wagner, A Galdran, Y Shi, W Xia, G Wang, X Mou, MA Ahamed, …
  Year: 2025
  Citations: 13

• Title: Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy
  Authors: Y Mori, EH Jin, D Lee
  Year: 2024
  Citations: 10

• Title: Practical training approaches for discordant atopic dermatitis severity datasets: merging methods with soft-label and train-set pruning
  Authors: SI Cho, D Lee, B Han, JS Lee, JY Hong, JH Chung, DH Lee, JI Na
  Year: 2022
  Citations: 10

• Title: Reliability of suprahyoid and infrahyoid electromyographic measurements during swallowing in healthy subjects
  Authors: MW Park, D Lee, HG Seo, TR Han, JC Lee, HC Kim, BM Oh
  Year: 2021
  Citations: 8

• Title: Essential elements of physical fitness analysis in male adolescent athletes using machine learning
  Authors: YH Lee, J Chang, JE Lee, YS Jung, D Lee, HS Lee
  Year: 2024
  Citations: 7

• Title: External testing of a deep learning model to estimate biologic age using chest radiographs
  Authors: JH Lee, D Lee, MT Lu, VK Raghu, JM Goo, Y Choi, SH Choi, H Kim
  Year: 2024
  Citations: 5

• Title: Effect of an anti-adhesion agent on vision-based assessment of cervical adhesions after thyroid surgery: randomized, placebo-controlled trial
  Authors: HW Yu, D Lee, K Lee, S Kim, YJ Chai, HC Kim, JY Choi, KE Lee
  Year: 2021
  Citations: 5

• Title: Augmented Reality-Based Visual Cue for Guiding Central Catheter Insertion in Pediatric Oncologic Patients
  Authors: JK Youn, D Lee, D Ko, I Yeom, HJ Joo, HC Kim, HJ Kong, HY Kim
  Year: 2022
  Citations: 4

• Title: Texture-preserving low dose CT image denoising using Pearson divergence
  Authors: J Oh, D Wu, B Hong, D Lee, M Kang, Q Li, K Kim
  Year: 2024
  Citations: 2

• Title: Optimal view detection for ultrasound-guided supraclavicular block using deep learning approaches
  Authors: Y Jo, D Lee, D Baek, BK Choi, N Aryal, J Jung, YS Shin, B Hong
  Year: 2023
  Citations: 2