Jongwoo Kim, PhD
Applied Clinical Informatics Branch
Expertise and Research Interests:
Dr. Jongwoo Kim is a staff scientist at the Lister Hill National Center for Biomedical Communications at the United States National Library of Medicine (NLM), National Institutes of Health (NIH). He received his Ph.D. at the University of Missouri, Columbia, majoring in Computer Engineering and Computer Science. His research interests include deep (machine) learning, computer vision, image processing, Fuzzy theory, and document image processing. Currently, he is developing several algorithms with the National Eye Institute (NEI) and other Institutes using deep learning and image processing technologies to detect and diagnose retinal diseases and abnormality from fundus, optical coherence tomography (OCT), and fluorescein angiography (FA) images. His current works focuses on three areas: First, he works on improving diagnosis of glaucoma through automatic segmentation of optic disc and cup, and estimation of measures (Cup to Disc ratio, Rim to Disc ratio, etc.) from fundus images. Second, he works on the classification and segmentation of OCT images based on diseases and defects. Third, he researches automatic segmentation of leakages and blood vessels from FA images for uveitis.
Previously, Dr. Kim was involved in several projects such as Medical Article Recording System (MARS), Web-based Medical Article Recording System (WebMARS), Publisher Data Review System (PDRS), and In-Memory Processing for Publisher Online Articles (IMPPOA) to develop document processing systems for biomedical journal articles. He led a team to design, develop, and maintain web-based MARS that extracts bibliographic information from hard-copy journal articles to populate MEDLINE® using WCF services and Web-based GUIs. He also led a team to design and develop a web-based systems (WebMARS, PDRS, and IMPPOA) to extract bibliographic information from full text online journal articles in publishers’ websites and PubMed Central® for MEDLINE®. In his research on both projects, he also developed several key algorithms to automatically extract the bibliographic information using statistics and machine learning algorithms such as Fuzzy theory, SVM, Bayesian, and deep learning.
Dr. Kim is a member of the Institute of Electrical and Electronics Engineers (IEEE). He serves on the member of the Editorial Board of the International Journal of Imaging Systems and Technology (IMA). He also works as a reviewer for several journals related to the medical/biomedical image processing areas.
Publications:Kim J, Tran L. Retinal Disease Classification from OCT Images Using Deep Learning Algorithms. IEEE-CIBCB 2021, pp. 42-47, Melbourne, Australia, October 2021
Young L, Kim J, Yakin M, Lin H, Dao D, Kodati S, Sharma S, Lee A, Lee C, Sen, HN. Automated detection of vascular leakage on fluorescein angiography. The Association for Research in Vision and Ophthalmology (ARVO) 2021, May 2021.
Sen N, Young L, Kim J, Sharma S, Lee A, Lee C. Fully Automated Algorithm to Detect Vascular Leakage in Uveitis. The 44th Virtual Annual Macula Society Meeting, ID 141, February, 2021.
Kim J, Tran L. Ensemble Learning Based on Convolutional Neural Networks for the Classification of Retinal Diseases from Optical Coherence Tomography Images. 2020 IEEE 33th International Symposium on Computer-Based Medical Systems (IEEE CBMS 2020), pp 535-540, Rochester, USA, July 2020.
Kim J, Tran L, Chew E, Antani SK. Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning. 2019 IEEE 32th International Symposium on Computer-Based Medical Systems (CBMS), pp 489-494, Cordoba, Spain, June 2019.
Kesav N, Yang Q, Losert W, Kim J, Jaeger S, Sen HN. Novel automated processing techniques of fluorescein angiography (FA) images in patients with Uveitis. Annual Meeting of the Association for Research in Vision and Ophthalmology (ARVO).
Kim J, Tran L, Chew E, Antani SK, Thoma GR. Optic Disc Segmentation in Fundus Images Using Deep Learning. SPIE Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, Vol. 10954, San Diego, USA, February 2019.
Rae A, Kim J, Le DX, Thoma GR. Main Content Detection in HTML Journal Articles. DocEng ’18: ACM Symposium on Document Engineering 2018, August 28–31, 2018, Halifax, NS, Canada. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3209280.3229115
Kim J, Candemir S, Chew E, Thoma GR. Region of Interest Detection in Fundus Images Using Deep Learning and Blood Vessel Information. The 31th IEEE International Symposium on Computer-Based Medical Systems. (IEEE CBMS 2018), pp. 357-362, Karlstad, Sweden, June 2018.
Kim J, Hong S, Thoma GR. Labeling Author Affiliations in Biomedical Articles Using Markov Model Classifiers. The 13th International Conference on Data Mining (DMIN2017), pp. 105-110, Las Vegas, USA, July 2017.
Kim J, Thoma GR. Named Entity Recognition in Affiliations of Biomedical Articles Using Statistics and HMM Classifiers. The 2016 International Conference on Data Mining (DMIN2016), Las Vegas, USA, pp. 236-241, July, 2016.
Kim J, Lobuglio PS, Thoma GR. Visualization of Statistics from MEDLINE. 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS 2016), Dublin and Belfast, Ireland, pp. 290-291, June, 2016.
Cohen T, Widdows D, Stephan C, Zinner R, Kim J, Rindflesch TC, Davies P. Predicting high-throughput screening results with scalable literature-based discovery methods. CPT Pharmacometrics Syst Pharmacol. 2014 Oct 8;3:e140. doi: 10.1038/psp.2014.37.
Kim J, Le DX, Thoma GR. Identification of Investigator Name Zones Using SVM Classifiers and Heuristic Rules. 12th international Conference on Document Analysis and Recognition (ICDAR). Washington D.C., August 2013.
Kim J, Le DX, Thoma GR. Combining SVM Classifiers to Identify Investigator Name Zones in Biomedical Articles. . IS&T/SPIE’s 22nd Annual Symposium on Electronic Imaging. San Francisco, CA, January 2012; 8297.
Meyerson JR, White TA, Bliss D, Moran A, Bartesaghi A, Borgnia MJ, de la Cruz JV, Schauder D, Hartnell LM, Nandwani R, Dawood M, Kim B, Kim JH, Sununu J, Yang L, Bhatia S, Subramaniam C, Hurt DE, Gaudreault L, Subramaniam S. Determination of molecular structures of HIV envelope glycoproteins using cryo-electron tomography and automated sub-tomogram averaging. J Vis Exp. 2011 Dec 1;(58). pii: 2770. doi: 10.3791/2770.
Mitra R, Lee J, Jo J, Milani M, McClintick J, Edenberg H, Kesler K, Rieger K, Badve S, Cummings O, Mohiuddin A, Thomas DG, Luo X, Juliar BE, Li L, Mesaros C, Blair IA, Srirangam A, Kratzke R, McDonald CJ, Kim J, Potter DA. Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model. Clin Cancer Res. 2011 May 1;17(9):2934-46. doi: 10.1158/1078-0432.CCR-10-1803. Epub 2011 Jan 17.
Kim J, Le DX, Thoma GR. Naive Bayes and SVM Classifiers For Classifying Databank Accession Number Sentences From Online Biomedical Articles. IS&T/SPIE's 22nd Annual Symposium on Electronic Imaging. San Jose, CA. January 2010;7534:75340U-1 - 8
Kim J, Le DX, Thoma GR. Inferring Grant Support Types From Online Biomedical Articles. 22nd IEEE ISCBMS. Albuquerque, NM. August 2009
Kim J, Le DX, Thoma GR. Naive Bayes Classifier for Extracting Bibliographic Information From Biomedical Online Articles. Proc 2008 International Conference on Data Mining. Las Vegas, Nevada, USA. July 2008;II:373-8
Thoma GR, Le DX, Kim I, Kim JW, Moon C, Tran L, Zou J. Automation to Accelerate the Production of MEDLINE. April 2008 Technical Report to the LHNCBC Board of Scientific Counselors.
Kim J, Le DX, Thoma GR. Automatic Extraction of Bibliographic Information from Biomedical Online Journal Articles Using a String Matching Algorithm. Proc IEEE CBMS, June 2006, Salt Lake City, Utah; 905-10
Kim J, Le DX, Thoma GR. Automated Labeling Of Biomedical Online Journal Articles. In: Callaos N, Lesso W, editors. SCI 2005. Proc 9th World Multiconference on Systemics, Cybernetics and Informatics; 2005 Jul 10-13; Vol. 4; Orlando (FL): International Institute of Informatics and Systemics; c2005. 406-11
Kim J, Le DX, Thoma GR. Automated Labeling for Biomedical Journals Published in Foreign Languages. Proc. 8th World Multiconference on Systemics, Cybernetics and Informatics. 2004 Jul.;:444-9.
Mao S, Kim J, Thoma G. A Dynamic Feature Generation System for Automated Metadata Extraction in Preservation of Digital Materials. Proc. International Workshop on Document Image Analysis for Libraries (DIAL2004). 2004 Jan;: 225-32.
Mao S, Kim J, Thoma G. Style-Independent Document Labeling: Design and Performance Evaluation. Proc. SPIE - Document Recognition and Retrieval. 2004 Jan;: 14-22.
Mao S, Kim J, Le DX, Thoma GR. Generating Robust Features for Style-Independent Labeling of Bibliographic Fields in Medical Journal Articles. Proc. 7th World Multiconference on Systemics, Cybernetics and Informatics.2003 July;III:53-6.
Kim J, Le DX, Thoma GR. Automated Labeling Algorithms for Biomedical Document Images. Proc. 7th World Multiconference on Systemics, Cybernetics and Informatics. 2003 July;V: 352-57.
Kim J, Le DX, Thoma GR. Automated Labeling in Document Images. Proc. SPIE, Document Recognition and Retrieval VIII. 2001 Jan;4307:111-22.
Le DX, Tran LQ, Chow J, Kim J, Hauser SE, Moon CW, Thoma GR. Automated Medical Citation Records Creation for Web-Based Online Journals. Proc. 14th IEEE Symposium on Computer-Based Medical Systems: IEEE Computer Society. 2001.
Kim J, Le DX, Thoma GR. Automated Labeling of Bibliographic Data Extracted from Biomedical Online Journals. Proc. SPIE Electronic Imaging. 2003 Jan;5010: 47-56.