PERSONNEL

MISSING

Loc Tran, MS

Applied Clinical Informatics Branch

Contact InformationNihbc 38A - Lister Hill 10s1011h 301.827.4776
lotran@mail.nih.gov


Expertise and Research Interests:

Mr. Tran is experienced in various areas: Research Scientist, Machine Learning / Deep Learning algorithm and software development, Object Oriented, Client/Server programming with Python, Visual C++, C#, VB, GUI and Web Page designs, Oracle Server, MS-SQL Server, and Networking in Windows environment. He is also experienced in IOS application development.

Mr. Tran received his master’s degree in Computer Science from George Mason University. He also has an Oracle DBA & Developer Certificate from Oracle Corp. and a System Engineer Certificate from EDS Corp. He is currently a Research Scientist, Deep Learning Software developer for several projects at ACIB.

Mr. Tran was previously designed and developed the applications for 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). He designed the architecture and developed a system called the WebMARS Assisted Indexing System (WAIS). The system assisted indexers to recognize the index-terms of medical journals.


Publications:

Kim J, Tran L. Ensemble Convolutional Neural Networks for the Classification and Visualization of Retinal Diseases in Optical Coherence Tomography Images. IEEE-CBMS 2023, pp. 123-28, L'Aquila, Italy, June 2023.

Kim J, Tran L, Peto T, Chew EY, HN. Deep Learning and Ensemble Method for Optic Disc and Cup Segmentation. IEEE CIBCB 2022, August 15-17, 2022. https://doi.org/10.1109/CIBCB55180.2022.9863022.

Kim J, Tran L, Peto T, Chew EY. Identifying Those at Risk of Glaucoma: A Deep Learning Approach for Optic Disc and Cup Segmentation and Their Boundary Analysis. Diagnostics 2022, 12(5), 1063; https://doi.org/10.3390/diagnostics12051063.

Kim J, Tran L. Retinal Disease Classification from OCT Images Using Deep Learning Algorithms. IEEE-CIBCB 2021, pp. 42-47, Melbourne, Australia, October 2021

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