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Leonard
R
Long
,
MA
Electronics Engineer
Location: 
38A
/
10S1008
Phone Number: (
301
) 
435-3208
E-mail: 
long@nlm.nih.gov
Expertise and Research Interests: 

Mr. L. Rodney Long leads a development group in creating applications for image-based biomedical information collection and dissemination. He is currently working in collaboration with the National Cancer Institute to develop a suite of tools for uterine cervix cancer databases.  His research interests are in telecommunications, systems biology, image processing, and scientific/biomedical databases.  He has an M.A. in applied mathematics from the University of Maryland.

Professional Organizations: 

He is a member of  the Mathematical Association of America (MAA) , the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computer Society.

Honors and Awards: 

Mr. Long has received numerous staff honors. In addition, he has received the NLM Regents Award for Scholarship and Technical Achievement (2002), the NIH Award of Merit (2007), the Federal Computer Week Fed 100 Award (2009), and the American Society for Colposcopy and Cervical Pathology (ASCCP) Award of Merit (2010).

Publications/Tools by Leonard Long: 
Xue Z, Long LR, Jaeger S, Folio L, Thoma GR. Extraction of Aortic Knuckle Contour in Chest Radiographs Using Deep Learning. EMBC 2018.
Abstract | PDF
AbstractDownload
Xue Z, Rajaraman S, Long LR, Antani SK, Thoma GR. Gender Detection from Spine X-ray Images Using Deep Learning. Proc. IEEE International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. pp. 54-58, DOI:10.1109/CBMS.2018.00017.
Abstract | PDF
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Sornapudi S, Stanley RJ, Stoecker WV, Almubarak H, Long LR, Antani SK, Thoma GR, Zuna R, Frazier SR. Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels. J Pathol Inform. 2018 Mar 5;9:5. doi: 10.4103/jpi.jpi_74_17. eCollection 2018.
Abstract | PMID:  29619277 | PMCID:  PMC5869967
Abstract
Almubarak H, Guo P, Stanley RJ, Long LR, Antani SK, Thoma GR. Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images. in Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics,. IGI Global (Hershey, PA).
Abstract
Abstract
Xue Z, Jaeger S, Antani SK, Long LR, Karargyris A, Siegelman J, Folio LR, Thoma GR. Localizing tuberculosis in chest radiographs with deep learning. SPIE Medical Imaging 2018
Abstract | PDF
AbstractDownload
Xue Z, Antani SK, Long LR, Thoma GR. Using deep learning for detecting gender in adult chest radiographs. SPIE Medical Imaging 2018
Abstract | PDF
AbstractDownload
de Herrera G, Long LR, Antani SK. Graph Representation for Content–based fMRI Activation Map Retrieval. Proceedings of 1st Life Sciences Conference, Sydney, Australia, December 2017 pp. 129-32 DOI: https://doi.org/10.1109/LSC.2017.8268160.
Abstract
Abstract
Bryant B, Sari-Sarraf H, Long LR, Antani SK. A Kernel Support Vector Machine Trained Using Approximate Global and Exhaustive Local Sampling. Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) 2017, Austin, Texas, USA, December 2017. Pp. 267-8 DOI: https://doi.org/10.1145/3148055.3149206
Abstract
Abstract
Almubarak HA, Stanley RJ, Long LR, Antani SK, Thoma GR, Zuna R, Frazier SR. Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images. Procedia Computer Science, Volume 114, 2017, Pages 281-287, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2017.09.044.
Abstract
Abstract
Xue Z, Antani SK, Long LR, Thoma GR. Automatic multi-label annotation of abdominal CT images using CBIR. Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 1013807 (March 13, 2017); doi:10.1117/12.2254368.
Abstract | PDF
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