Dong Hee Lee

I'm a research technician (project academic specialist) at International Research Center for Neurointelligence (IRCN) in Japan 🗼, 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

Publications

Main papers are highlighted.

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.

3 CITATIONS 3 Total citations 3 Recent citations n/a Field Citation Ratio n/a Relative Citation Ratio

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.

2 CITATIONS 2 Total citations 2 Recent citations n/a Field Citation Ratio n/a Relative Citation Ratio

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.

17 CITATIONS 17 Total citations 13 Recent citations 5.62 Field Citation Ratio 1.48 Relative Citation Ratio

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.

53 CITATIONS 53 Total citations 39 Recent citations 20 Field Citation Ratio 4.18 Relative Citation Ratio

Projects

Unpublished works and personal projects.

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.