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Sameer
K
Antani
,
PhD
Branch Chief
Location: 
38A
/
10S1004
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: 
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. 2020, [Online].
Rajaraman S, Kim I, Antani SK. Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles. PeerJ 8:e8693 https://doi.org/10.7717/peerj.8693
Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears. To be presented at SPIE Medical Imaging, Feb.18-20, 2020, Houston, USA.
Rajaraman S, Antani SK. Modality-Specific Deep Learning Model Ensembles Toward Improving TB Detection in Chest Radiographs. IEEE Access, vol. 8, pp. 27318-27326, 2020.
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.
Cheng P, Lu P, Wang P, Zhou W, Yu W, Jaeger S, Li J, Wu T, Ke X, Zheng B, Antani SK, Candemir S, Quan S, Lure F, Li H, Guo L. Applying Deep Learning and Radiomics to Determine Biological Lung and Heart Age from Chest Radiographs. Chinese Congress of Radiology.
Wang X, Guan Y, Lu P, Cheng G, Zhou W, Jaeger S, Zhen B, Antani SK, Yin X, Yu W, Guo L, Quan S, Lure F, Hurt D, Gabrielian A, Li H, Ke X. Screening of Tuberculosis in a TB High-burden Large Rural Region in China with Deep Learning Multi-modality Artificial Intelligence. Chinese Congress of Radiology.
Yu H, Yang F, Silamut R, Maude S, Jaeger S, Antani SK. Automatic Blood Smear Analysis with Artificial Intelligence and Smartphones. ASTMH 68th Annual Meeting, Washington DC, Nov. 20-24, 2019.
Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ. doi: 10.7717/peerj.6977.
Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN. Proceedings of AIPR2019, Washington DC, USA, Oct 15-17, 2019.

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