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Branch Chief
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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: 
Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK. Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays. IEEE Access, vol. 8, pp. 115041-115050, 2020, doi: 10.1109/ACCESS.2020.3003810
Rajaraman S, Sornapudi S, Alderson PO, Folio LR, Antani SK. Interpreting Deep Ensemble Learning through Radiologist Annotations for COVID-19 Detection in Chest Radiographs. medRxiv 2020.07.15.20154385; doi:
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.
Alzamzmi GA, Hsu L, Li W, Sachdev V, Antani SK. Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions. IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2020.2988295
Alzamzmi GA, Hsu L, Li W, Sachdev V, Antani SK. Fully automated spectral envelope and peak velocity detection from Doppler echocardiography images. Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113144G (16 March 2020);
Alzamzmi GA, Hsu L, Li W, Sachdev V, Antani SK. Echo Doppler Flow Classification and Goodness Assessment with Convolutional Neural Networks. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 1744-1749, doi: 10.1109/ICMLA.2019.00283
Jain SK, Andronikou S, Goussard P, Antani SK, Gomez-Pastrana D, Delacour C, Starke JR, Ordonez AA, Jean-Philippe P, Browning RS, Perez-Velez C. Advanced imaging tools for childhood tuberculosis: potential applications and research needs. . The Lancet Infectious Diseases. DOI:
Alzamzmi GA, Rajaraman S, Antani SK. Accelerating Super-Resolution and Visual Task Analysis in Medical Images. Appl. Sci. 2020, 10, 4282.
Alzamzmi GA, Rajaraman S, Antani SK. Unified Representation Learning for Efficient Medical Image Analysis 2020, [Online]
Rajaraman S, Antani SK. Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays. Diagnostics 2020, 10, 358.