Jungro Lee

Jungro Lee is in the 4th year of Electrical and Electronics Engineering at Chung-Ang University. Lee's main interests are Medical AI, applying my computer vision skills(diffusion, segmentation, etc) to a variety of medical domain. He is currently working on several projects related to medical image segmentation, utilizing the Attention map of data, and improving efficiency of 3D-implicit representation model for tooth data finding optimal sampling method. In addition to these topics, he is also interested of integrating medical data analysis,(SHAP, PCA, tsne) and AI to solve problems in psychology, such as Imsonia, Epilepsy, Parasomnias, etc.

- Proficiency in Python for rapid implementation of an idea
- High ability to understand new knowledge
- Applying artificial intelligence model to various domains (medical image, 3D data, etc.)
- Communication skills for team seminars

- Computer Vision
- Signal Data Analysis from Wearable Devices
- Deep Learning in Medical Imaging (MRI, CT, PET)
- Solving Clinical Challenges through Deep Learning

- Kickboxing, watching UFC
- Playing Soccer
- Online Game(especially League of Legends!)
- Listening to music, Enjoying concert
- Travlel the world!

Education

Internship

Publication

Jungro Lee, Royhyeok Choi, Junmin Kim, Minhyeok Lee Tooth-PointNet: Optimal Point Sampling for Dental Classification (Submitted, First Author)

S. Roy Choi, Jungro Lee and M. Lee, "OrgUNETR: Utilizing Organ Information and Squeeze and Excitation Block for Improved Tumor Segmentation," in IEEE Access, vol. 12, pp. 84122-84133, 2024, doi: 10.1109/ACCESS.2024.3413717. keywords: {Tumors;Image segmentation;Task analysis;Computer architecture;Transformers;Feature extraction;Computational modeling;Deep learning;Organ segmentation;tumor segmentation;medical segmentation;deep learning;squeeze and excitation network;transformer},
(IF: 3.9, JCR(%):36.2%, Q1 in Engineering)

S. R. Choi, K. Ko, S. J. Baek, S. Lee, J. Lee and M. Lee, "Enhanced Kidney Tumor Segmentation in CT Scans Using a Simplified UNETR with Organ Information," 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Osaka, Japan, 2024, pp. 1-5, doi: 10.1109/ICAIIC60209.2024.10463270.

Lee Jungro, & Lee Minhyeok (2023-11-22). Machine Learning-based Classification of Exercise Activities Using Electromyography (EMG) Sensor-derived Electrical Signals. Proceedings of Symposium of the Korean Institute of communications and Information Sciences.

* marked authors had equal contribution