Junghwa Kang

Ph.D Student

Department of Biomedical Engineering, Hankuk University of Foreign Studies

I am a Ph.D student advised by Professor Yoonho Nam, at Department of Biomedical Engineering, Hankuk University of Foreign Studies (HUFSAIM Lab)

I am broadly interested in medical image processing and deep learning. Specifically, my research interests include

MRI
Image Processing
Medical Image AI
Segmentation
Glymphatic system

Contact

✉️ kangjung9592@gmail.com

✉️ jhkang@hufs.ac.kr

Education

🎓 Ph.D, Hankuk University of Foreign Studies, 2022 ~ Present
Hankuk University of Foreign Studies
Advisor: Prof. Yoonho Nam

🎓 MS, Hankuk University of Foreign Studies, 2020~2022
Hankuk University of Foreign Studies
Advisor: Prof. Yoonho Nam

🎓 BS, Double Major in Division of Computer & Electronic Systems Engineering and Department of Biomedical Engineering, 2016 ~ 2020
Hankuk University of Foreign Studies

🎉 Recent News

[2026/5/9] ISMRM 2026 Summa Cum Laude

[2025/10/31] Present ICMRI 2025 Education session (Automatic Perivascular Space Quantification Method)

💡 Recent Conference

Age-Conditioned Neonatal Choroid Plexus Segmentation using Adaptive Conditional Instance Normalization

J Kang et al. Hankuk University of Foreign Studies, Korea

ISMRM, 2026, Oral Presentation (Summa Cum Laude)

Prediction of breast cancer recurrence based on automatically extracted quantitative MR features

J Kang et al. Hankuk University of Foreign Studies, Korea

ISMRM, 2025, Oral Presentation (Magna Cum Laude)

Automatic Lateral Ventricle and Choroid Plexus segmentation Method in Infant Brain MR Images

J Kang et al. Hankuk University of Foreign Studies, Korea

ISMRM, 2025, Digital Poster

💡 Publication

• Kang, J., Kim, H. G., Shin, N. Y., & Nam, Y. (2026). Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images. BMC Medical Imaging link

• Kang, J., Bak, D., Shin, N. Y., Kim, H. G., & Nam, Y. (2026). Improved BG‐PVS Quantification in Infant Brain MRI Using Anatomy‐Informed Pseudo‐Labels for Joint BG and PVS Segmentation. Journal of Magnetic Resonance Imaging. link

• Park, G. E., Kim, S. H., Nam, Y., Kang, J., Park, M., & Kang, B. J. (2024). 3D Breast Cancer Segmentation in DCE‐MRI Using Deep Learning with Weak Annotation. Journal of Magnetic Resonance Imaging. link

• Kim, H., Jang, J., Kang, J., Jang, S., Nam, Y., Choi, Y., ... & Kim, B. S. (2022). Clinical implications of focal mineral deposition in the globus pallidus on CT and quantitative susceptibility mapping of MRI. Korean Journal of Radiology, 23(7), 742. link

• Nam, Y., Choi, Y., Kang, J., Seo, M., Heo, S. J., & Lee, M. K. (2022). Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks. Scientific Reports, 12(1), 21510. link

• Kim, W., Shin, H. G., Lee, H., Park, D., Kang, J., Nam, Y., ... & Jang, J. (2022). χ-Separation Imaging for Diagnosis of Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disorder. Radiology, 220941. link

• Kang, J., & Nam, Y. (2022). Applications of Artificial Intelligence in MR Image Acquisition and Reconstruction. Journal of the Korean Society of Radiology, 83(6), 1229-1239. link

• Lee, J. H., Kang, J., Oh, S. H., & Ye, D. H. (2022). Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction. Sensors, 22(10), 3943. link

• Kang, J., Kim, H., Kim, E., Kim, E., Lee, H., Shin, N. Y., & Nam, Y. (2021). Convolutional Neural Network-Based Automatic Segmentation of Substantia Nigra on Nigrosome and Neuromelanin Sensitive MR Images. Investigative Magnetic Resonance Imaging, 25(3), 156-163. link

• Nam, Y., Park, G. E., Kang, J., & Kim, S. H. (2020). Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine‐Learning Models. Journal of Magnetic Resonance Imaging. Link / GitHub

💡 Awards & Honor

• 2026 ISMRM Annual Meeting & Exhibition Summa Cum Laude

• 2025 ISMRM Annual Meeting & Exhibition Magna Cum Laude

• 2021, 2022, 2023, 2025 ISMRM Annual Meeting & Exhibition Stipend

• 2024 MICCAI Enlarged Perivascular Spaces Segmentation challenge 3rd Place

• 2024 ISMRM, Imaging Neurofluids Study group TRAINEE AWARD 2nd Place Award

• MICCAI 2021 MSSEG2 challenge participation. Presentation / ShortPaper

- Short paper: Segmentation of New Multiple Sclerosis Lesions in Longitudinal MRI Analysis Using a Multi-Stage 3D Patch-wise Deep Learning Algorithm

• MOAI 2020 Body morphometry AI segmentation challenge
Final ranking: 1st Place

PPT / GitHub

• Healthcare AI Learning Platform (HeLP) Challenge 2019
Task: Traumatic lesion classification and detection
Final ranking: 1st Place
PPT / Presentation / GitHub

💡 Academic Activities

Reviewer

MICCAI 2024, 2025, 2026

• Medical Image Processing & Laboratory using Artificial Intelligence (Fall, 2020 / 2021 / 2022 / 2023 / 2024)

• Signal and system (Spring, 2024)

• Biomedical Artificial Intelligence (Spring, 2021/2022/2023)

• Biomedical Probability & Statistics (Spring, 2020)

• Logic circuit & Lab (Spring, 2019)