The goal of our research is to advance our understanding of diseases using advanced imaging methods combined with artificial intelligence technology. Our laboratory has two main research focus: 1) Development of machine learning/deep learning-based methods for application to clinical data, including medical imaging and EMR (electronic medical record), 2) Development of new imaging techniques and imaging biomarkers using magnetic resonance imaging (MRI)
Associate Professor, Chonnam National University
Department of Artificial Intelligence Convergence, College of Engineering
Department of Radiology, College of Medicine
BA, University of California Berkeley
PhD, University of California Berkeley
Email: ipark@chonnam.edu, ipark@jnu.ac.kr
Le BD, Oh K-J, Le AT, Hoang L, Park I. Investigation and quantification of composition variability in urinary stone analysis. Investig Clin Urol . 2024 Sep;65(5):511-517. doi: 10.4111/icu.20240275.
Le BD, Heo SH, Chung HS, Park I. Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning. Int J Med Inform. 2024 Jul:187:105467. doi: 10.1016/j.ijmedinf.2024.105467
Nguyen TTY, Nguyen A-T, Kim H, Jung YJ, Park W, Kim KM, Park I & Kim W. Deep-learning model for evaluating histopathology of acute renal tubular injury. Sci Rep 14, 9010 (2024). https://doi.org/10.1038/s41598-024-58506-9
Le BD, Nguyen TA, Baek BH, Oh K-J, Park I. Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning. World J Urol. 2024 Mar 13;42(1):150. doi: 10.1007/s00345-024-04818-4
Park JH, Quang LT, Yoon W, Baek BH, Park I, Kim SK. Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI. Biomedicines. 2023 Dec 10;11(12):3268. doi.org/10.3390/biomedicines11123268
Lee HJ, Nguyen AT, Ki SY, Lee JE, Do LN, Park MH, Lee JS, Kim HJ, Park I, Lim HS. Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning. Front Oncol. 2021;11:744460. doi: 10.3389/fonc.2021.744460.
Lee SJ, Park I, Talbott JF, Gordon J. Investigating the Feasibility of In Vivo Perfusion Imaging Methods for Spinal Cord Using Hyperpolarized [13C]t-Butanol and [13C,15N2]Urea. Mol Imaging Biol. 2021 Nov 15;. doi: 10.1007/s11307-021-01682-1.
Park I, Kim S, Pucciarelli D, Song J, Choi JM, Lee KH, Kim YH, Jung S, Yoon W, Nakamura JL. Differentiating Radiation Necrosis from Brain Tumor Using Hyperpolarized Carbon-13 MR Metabolic Imaging. Mol Imaging Biol. 2021 Jun;23(3):417-426. doi: 10.1007/s11307-020-01574-w.
Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, Chae JB, Huh CH, Na JI. Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. J Invest Dermatol. 2020 Sep;140(9):1753-1761. doi: 10.1016/j.jid.2020.01.019.
Park I, Larson PEZ, Gordon JW, Carvajal L, Chen HY, Bok R, Van Criekinge M, Ferrone M, Slater JB, Xu D, Kurhanewicz J, Vigneron DB, Chang S, Nelson SJ. Development of methods and feasibility of using hyperpolarized carbon-13 imaging data for evaluating brain metabolism in patient studies. Magn Reson Med. 2018 Sep;80(3):864-873. doi: 10.1002/mrm.27077.
Artificial Intelligence in Medical Imaging
Medical Physics
Python and Deep Learning Bootcamp
How to Start Deep Learning Research as a Radiologist
Practical Introduction to Deep Learning for Radiologists
We are seeking highly motivated research associates, mater and phD students, who are interested in machine/deep learning applications to medical imaging as well as developing new metabolic MRI techniques for probing disease metabolism. If you are interested, please contact Dr. Park at ipark@jnu.ac.kr