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Sivaramakrishnan
Rajaraman
,
PhD
Research Scientist
Location: 
38A
/
10N1003K
Phone Number: (
301
827-2383
Expertise and Research Interests: 

Dr. Sivarama Krishnan Rajaraman joined the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM), National Institutes of Health (NIH), as a postdoctoral researcher in 2016. Dr. Rajaraman received his Ph.D. in Information and Communication Engineering from Anna University, Chennai, India. He is involved in projects that aim to apply computational sciences and engineering techniques toward advancing life science applications. These projects involve the use of medical images for aiding healthcare professionals in low-cost decision-making at the point of care screening/diagnostics. He is a versatile researcher with expertise in machine learning, data science, biomedical image analysis/understanding, and computer vision. He has more than 15 years of experience in academia where he taught core and allied subjects in biomedical engineering. He has authored several national and international journal and conference publications in his area of expertise. He is being mentored by Sameer Antani, PhD., Staff Scientist, Communications Engineering Branch, NLM, NIH.

Honors and Awards: 

Dr. Rajaraman received the NLM Special Acts/Services Group Award in 2018. He is placed in the top 1% of reviewers consecutively on Publons’ global reviewer database for the award years 2017-18 and 2018-19. This award is determined by the number of peer review reports performed during the given award year.

Publications/Tools by Sivaramakrishnan Rajaraman: 
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: https://doi.org/10.1101/2020.07.15.20154385.
Alzamzmi GA, Rajaraman S, Antani SK. Unified Representation Learning for Efficient Medical Image Analysis 2020, [Online]
Alzamzmi GA, Rajaraman S, Antani SK. Accelerating Super-Resolution and Visual Task Analysis in Medical Images. Appl. Sci. 2020, 10, 4282.
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.
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
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.
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.
Rajaraman S, Candemir S, Xue Z, Alderson P, Thoma G, Antani SK. A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs. In Santosh KC et al. (Eds.). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. (pp. 1-26). New York, NY: CRC Press, Taylor & Francis Group.

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