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