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NLM Intramural Research Program

About

Picture of Sameer Antani, PhD

Sameer Antani, PhD

Tenure Track Investigator, Computational Health Research Branch, LHNCBC

Research in machine learning and artificial intelligence, medical image and signal processing, and information retrieval for solving challenging problems and advancing health.

Contact Information

Building 38A, 8600 Rockville Pike MSC 6075 Bethesda, MD 20894

Tel: (301) 435-3218

sameer.antani@nih.gov

Research Interests

Dr. Antani is a versatile senior researcher with expertise in machine learning and artificial intelligence (ML/AI), biomedical image informatics, image processing and computer vision, and information retrieval. His research studies the use of Ml/AI as a part of his interest in advancing computational sciences and engineering in biomedical research, education, and clinical care. In addition to advancing AI techniques for decision making and analysis of large biomedical data, his research extends to applying lessons learned on important health problems through trans-NIH collaborations, e.g., NCI (cervical cancer), NIAID (HIV/TB in adults and children, and drug resistant variants), among others. Dr. Antani is a senior member of the International Society of Photonics and Optics (SPIE), the Institute of Electrical and Electronics Engineers (IEEE). He serves as the vice chair for computational medicine on the IEEE Computer Society's Technical Committee on Computational Life Sciences (TCCLS) and the IEEE Life Sciences Technical Community (LSTC).

Publications

Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. doi: 10.1109/JBHI.2020.3034863. Epub 2021 May 11. PubMed PMID: 33119516; PubMed Central PMCID: PMC8127616.

Rajaraman S, Zamzmi G, Folio L, Alderson P, Antani S. Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings. Diagnostics (Basel). 2021 May 7;11(5). doi: 10.3390/diagnostics11050840. PubMed PMID: 34067034; PubMed Central PMCID: PMC8151767.

Rajaraman S, Folio LR, Dimperio J, Alderson PO, Antani SK. Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations. Diagnostics (Basel). 2021 Mar 30;11(4). doi: 10.3390/diagnostics11040616. PubMed PMID: 33808240; PubMed Central PMCID: PMC8065621.

Guo P, Xue Z, Jeronimo J, Gage JC, Desai KT, Befano B, García F, Long LR, Schiffman M, Antani S. Network Visualization and Pyramidal Feature Comparison for Ablative Treatability Classification Using Digitized Cervix Images. J Clin Med. 2021 Mar 1;10(5). doi: 10.3390/jcm10050953. PubMed PMID: 33804469; PubMed Central PMCID: PMC7957626.

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