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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).


Rajaraman S, Kim I, Antani S. Evaluating and visualizing the learned behavior of convolutional neural network ensembles toward for abnormality detection in chest radiographs. PeerJ. Forthcoming; 8:e8693. doi:

Sornapudi S, Hagert J, Stanley R, Stoecker WV, Long R, Antani S, Thoma G, Zuna R, Frazier SR. EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images. J Pathology Informatics. 2020 March; 11(10). doi: 10.4103/jpi.jpi_53_19.

Rajaraman S, Antani S. Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs. IEEE Access. 2020 February; 8:27318-26. doi: 10.1109/ACCESS.2020.2971257.

Zou J, Thoma G, Antani S. Unified Deep Neural Network for Segmentation and Labeling of Multipanel Biomedical Figures. Journal of the Association for Information Science and Technology. 2020 January; doi: 10.1002/asi.24334.