Dong Hee Lee

I'm a research technician (project academic specialist) at International Research Center for Neurointelligence (IRCN) in Japan πŸ—Ό, where I'm working on predictive-coding inspired neural networks and emotion recognition using ECG with Yukie Nagai.

I received my M.S. in Biomedical Engineering from Sungkyunkwan University in South Korea, where I worked with Choong-Wan Woo on computational decoding models of pain using human fMRI.

I'm interested in computational neuroscience, with a current focus on bayesian inference, generative models, transfer learning, and reinforcement learning.

E-mail  /  CV  /  Scholar  /  Github  /  LinkedIn

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Publications

Main papers are highlighted.

Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models
Dong Hee Lee (Lead author), Sungwoo Lee, Choong-Wan Woo
PAIN, 2025  
codes / commentary

This study shows a comprehensive overview about modeling targets and options of brain imaging-based pain predictive models through a literature review and benchmark analysis.

Interindividual differences in pain can be explained by fMRI, sociodemographic, and psychological factors
Suhwan Gim, Dong Hee Lee, Sungwoo Lee, Choong-Wan Woo
Nature Communications, 2024

As a commentary paper for Hoeppli et al. (2022), this paper examined that a multiple regression model including brain imaging, sociodemographic, and psychological measures could predict individual differences in pain self-report. We also identified brain regions associated with these differences using fMRI data.

Functional brain reconfiguration during sustained pain
Jae-Joong Lee, Sungwoo Lee, Dong Hee Lee, Choong-Wan Woo
eLife, 2022
codes

This study explored dynamic changes in functional brain networks during pain in the mouth, which provides new insights into how dynamic interactions between brain systems organize and modulate the experience of pain.

Individual variability in brain representations of pain
Lada KohoutovΓ‘, Lauren Y. Atlas, Christian BΓΌchel, Jason T. Buhle, Stephan Geuter, Marieke Jepma, Leonie Koban, Anjali Krishnan, Dong Hee Lee, Sungwoo Lee, Mathieu Roy, Scott M. Schafer, Liane Schmidt, Tor D. Wager & Choong-Wan Woo
Nature Neuroscience, 2022
codes

This study identified different brain representations of pain processing across brain regions. Some brain regions such as vmPFC show high variability across individuals, whereas other regions such as SMC show stable patterns.

Projects

Unpublished works and personal projects.

Automated heart sound segmentation: a deep learning web platform for cardiac diagnostics
Dong Hee Lee (Team Leader), Myungjun Lee, Junghyun Kim
2024.1.~2024.2. (7 weeks)
codes / presentation (Korean)

The goal of this project is to develop a heart sound segmentation deep learning model and web app service for assisting heart checkup. We developed a heart sound (S1, S2) segmentation deep learning model based on U-Net++ with stethoscope sound data provided by the PhysioNet Challenge and deployed it as a web app service. We preprocessed the audio data and converted it into spectrogram images, and then trained deep learning models and evaluated performance of the image segmentation.

Skills: Python, Numpy, Librosa, torchaudio, OpenCV, Tensorflow, Keras, Google Cloud Platform


Thanks to Jon Barron for providing the template of this website.