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Sameer
K
Antani
,
PhD
Branch Chief
Location: 
38A
/
10S1010
Phone Number: (
301
435-3218
Expertise and Research Interests: 

Dr. Antani is a versatile lead researcher advancing the role of computational sciences and automated decision making in biomedical research, education, and clinical care. His research interests include topics in medical imaging and informatics, machine learning, data science, artificial intelligence, and global health. He applies his expertise in machine learning, biomedical image informatics, automatic medical image interpretation, data science, information retrieval, computer vision, and related topics in computer science and engineering technology His primary R&D areas include cervical cancer, HIV/TB, and visual information retrieval, among others. Google Scholar.

Dr. Antani is currently also (Acting) Branch Chief for the Communications Engineering Branch and the Computer Science Branch in the Lister Hill National Center for Biomedical Communications at the National Library of Medicine.

Professional Activities: 

Dr. Antani is a Senior Member of the International Society of Photonics and Optics (SPIE), Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computer Society. He serves as the Vice Chair for Computational Medicine on the IEEE Technical Committee on Computational Life Sciences (TCCLS) and the IEEE Life Sciences Technical Community (LSTC). Dr. Antani currently serves on the editorial boards of the MDPI Journal Data and the Elsevier Journal Heliyon.

Honors and Awards: 

In addition to many staff achievement awards, in 2016, Dr. Antani received the NIH Director’s Award -- “For exemplary leadership and creative engineering in developing an automated chest x-ray screening system for tuberculosis and deploying it in Africa”. In 2016, he also received the Federal Computer Weekly - Federal 100 Award. 2015, Dr. Antani received the Information Technology Excellence Award from the Food and Drug Administration (FDA) - Center for Drug Evaluation and Research (CDER) along with other RAPID Project Team members for OTS Data Mining for developing a mobile application “that uses modern technology for real time adverse event reports and management in FDA”. In 2013, he received the NIH Award of Merit for his contribution to novel image and text based methods for searching the biomedical literature. In 2012, he received the NIH Award of Merit for his contributions to novel ways of search biomedical literature using visual and text queries in the Open-i® project. In 2009, he received the NIH Award of Merit for his contributions to Content-Based Image Retrieval in Geographically Distributed Systems. In 2008, he was a member of the NLM team recognized by Internet2 for developing geography-independent cancer research tools.

Publications/Tools by Sameer Antani: 
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.
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.
Rajaraman S, Sornapudi S, Kohli M, Antani SK. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 3689 – 3692.
Rajaraman S, Antani SK. Visualizing Salient Network Activations in Convolutional Neural Networks for Medical Image Modality Classification. Santosh K., Hegadi R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore
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.
Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977
Kim I, Rajaraman S, Antani SK. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. Diagnostics (Basel). 2019 Apr 3;9(2). pii: E38. doi: 10.3390/diagnostics9020038.
Kim I, Rajaraman S, Antani SK. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. Diagnostics, 9(2), 38, 2019.
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.
Rajaraman S, Candemir S, Thoma G, Antani SK. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs. Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500S (13 March 2019); doi: 10.1117/12.2512752.

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