Skip to main content

NLM Intramural Research Program


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).


Zamzmi G, Hsu LY, Li W, Sachdev V, Antani S. Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions. IEEE Rev Biomed Eng. 2021;14:181-203. doi: 10.1109/RBME.2020.2988295. Epub 2021 Jan 22. PubMed PMID: 32305938.

Xue Z, Novetsky AP, Einstein MH, Marcus JZ, Befano B, Guo P, Demarco M, Wentzensen N, Long LR, Schiffman M, Antani S. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int J Cancer. 2020 Nov 1;147(9):2416-2423. doi: 10.1002/ijc.33029. Epub 2020 May 19. PubMed PMID: 32356305.

Ye J, Xue Y, Long L, Antani S, Xue Z, Cheng K, Huang X. Synthetic Sample Selection via Reinforcement Learning. Proceedings MICCAI 2020, Lecture Notes in Computer Science (LNCS). 2020 October; 12261:53-63. doi: 10.1007/978-3-030-59710-8_6.

Rajaraman S, Antani S. Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays. Diagnostics (Basel). 2020 May 30;10(6). doi: 10.3390/diagnostics10060358. PubMed PMID: 32486140; PubMed Central PMCID: PMC7345787.