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Leonard
R
Long
,
MA
Electronics Engineer
Location: 
38A
/
10S1008
Phone Number: (
301
435-3208
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: 
Guo P, Xue Z, Mtema Z, Yeates K, Ginsburg O, Demarco M, Long LR, Schiffman M, Antani SK. 2. Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening. Diagnostics (Basel) 2020 Jul 3;10(7):451.
Xue Z, Novetsky A, Einstein M, Marcus J, Befano B, Guo P, Demarco M, Wenzenten N, Long LR, Schiffman M, Antani SK. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int J Cancer . 2020 Apr 30
Sornapudi S, Brown G, Xue Z, Long LR, Allen L, Antani SK. Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image. AMIA Annu Symp Proc. 2019; 2019: 820–827.
Guo P, Xue Z, Long LR, Antani SK. 1. Anatomical landmark segmentation in uterine cervix images using deep learning. Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131810 (2 March 2020)
Zou J, Xue Z, Brown G, Long LR, Antani SK. Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology. Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131811 (2 March 2020); doi: 10.1117/12.2549547
Guo P, Xue Z, Long LR, Antani SK. Cross-Dataset Evaluation of Deep Learning Networks for Uterine Cervix Segmentation. Diagnostics (Basel). 2020 Jan 14;10(1). pii: E44. doi: 10.3390/diagnostics10010044.
Xue Y, Zhou Q, Ye J, Long LR, Antani SK, Cornwell C, Xue Z, Huang X. Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification. ArXiv, abs/1907.10655.
Ganesan P, Rajaraman S, Long LR, Ghoraani B, Antani SK. Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 841 – 844.
Ganesan P, Xue Z, Singh S, Long LR, Ghoraani B, Antani SK. Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 4487 – 4490.
Guo P, Singh S, Xue Z, Long LR, Antani SK. Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) DOI: 10.1109/BHI.2019.8834495.

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