You are here

Printer-friendly versionPrinter-friendly version
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 was also placed in the top 1% of reviewers on Publons’ global reviewer database, determined by the number of peer review reports performed during the 2017-18 award year.

Publications/Tools by Sivaramakrishnan Rajaraman: 
Ganesan P, Rajaraman S, Long LR, Ghoraani B, Antani SK. Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 841 – 844.
Rajaraman S, Sornapudi S, Kohli M, Antani SK. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 3689 – 3692.
Rajaraman S, Antani SK. Visualizing Salient Network Activations in Convolutional Neural Networks for Medical Image Modality Classification. Santosh K., Hegadi R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore
Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977
Kim I, Rajaraman S, Antani SK. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. Diagnostics (Basel). 2019 Apr 3;9(2). pii: E38. doi: 10.3390/diagnostics9020038.
Kim I, Rajaraman S, Antani SK. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. Diagnostics, 9(2), 38, 2019.
Rajaraman S, Candemir S, Thoma G, Antani SK. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs. Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500S (13 March 2019); doi: 10.1117/12.2512752.
Candemir S, Rajaraman S, Thoma GR, Antani SK. Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation. Proc. IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28 – 30 October 2018. pp. 109-113.
Rajaraman S, Candemir S, Kim I, Thoma GR, Antani SK. Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs. Appl. Sci. 2018, 8, 1715.
Ben Abacha A, Gayen S, Lau JJ, Rajaraman S, Demner-Fushman D. NLM at ImageCLEF 2018 Visual Question Answering in the Medical Domain. CLEF2018 Working Notes. CEUR Workshop Proceedings, Avignon, France, CEUR-WS.org (September 10-14 2018).

Pages