Executive Summary
Dr. Sivarama Krishnan Rajaraman works as a Medical Imaging Deep Learning Research Scientist currently contracted by Guidehouse Digital LLC to serve at the Department of Intramural Research, at the National Library of Medicine (NLM), part of the U.S. National Institutes of Health (NIH). Holding a Ph.D. in Information and Communication Engineering from Anna University, India, Dr. Rajaraman brings over 15 years of academic and research experience to his role, where he leads pioneering projects in medical image analysis and artificial intelligence (AI). At the NLM, Dr. Rajaraman has been instrumental in developing robust, cost-effective AI solutions that significantly enhance clinical decision-making processes. His work focuses on advancing computational sciences and engineering methods to support life science applications, directly aiding healthcare professionals in delivering low-cost, high-quality screening and diagnostics at the point of care. Dr. Rajaraman is a versatile researcher with expertise in deep learning, biomedical image analysis, and medical computer vision. His scholarly contributions are substantial, with an extensive publication record that includes articles in top-tier national and international journals and conferences. Dr. Rajaraman’s work has garnered a citation count of 3,500, reflecting a cumulative h-index of 27 and an i10-index of 41, underscoring his influence and recognition within the scientific community. In addition to his research contributions, Dr. Rajaraman serves on the editorial boards of premier journals such as PLOS ONE, PLOS Digital Health, and PeerJ Computer Science. He is actively involved in organizing special issues and conference workshops, further demonstrating his leadership and commitment to advancing his field. His role as a peer reviewer for over 100 prestigious journals and conferences highlights his expertise and the high regard in which he is held by his peers. Dr. Rajaraman is also a member of several esteemed professional organizations, including the Society of Photo-Optical Instrumentation Engineers (SPIE) (Life Member), the Institute of Electrical and Electronics Engineers (IEEE) (Senior Member), and the IEEE Engineering in Medicine and Biology Society (EMBS) (Senior Member). These memberships are indicative of his standing in the professional community and his ongoing commitment to staying at the forefront of technological advancements in medical imaging and AI.
Education and Training
| Institution | Degree | Graduation Year | Field of Study |
|---|---|---|---|
| Anna University, Chennai, India | Ph.D. | 2015 | Information and Communication Eng. |
| College of Engineering, Chennai, India | M.E. | 2006 | Medical Electronics |
| PSNACET, MK University, Madurai, India | B.E. | 2001 | Electronics and Communication Eng. |
Research Interests
Artificial Intelligence, Deep Learning, Machine Learning, Medical Image Processing, Computer Vision.
Positions and Employment
| Job Title | Employment | Location |
|---|---|---|
| Medical Imaging Deep Learning Research Scientist | 10/24/2018 – Present | Guidehouse Digital LLC, Virginia, USA |
| Postdoctoral Researcher | 12/13/2016 – 10/23/2018 | National Library of Medicine, Bethesda, Maryland, USA |
| Associate Professor, Dept. of Biomedical Eng. | 06/01/2015 – 12/02/2016 | SSN College of Engineering, Tamil Nadu, India |
| Assistant Professor, Dept. of Biomedical Eng. | 06/02/2008 – 05/31/2015 | SSN College of Engineering, Tamil Nadu, India |
| Assistant Professor, Dept. of Electronics and Communication Eng. | 11/22/2002 – 06/01/2008 | Adhiparasakthi Engineering College, Tamil Nadu, India |
| Lecturer, Dept. of Electronics and Communication Eng. | 06/20/2001 – 05/30/2002 | PSNA College of Engineering and Technology, Tamil Nadu, India |
Research Projects
Advancing COVID-19 Detection via AI on Chest X‑Rays
Developed self-supervised contrastive learning with vision transformers for automated quantification of COVID-19 severity on frontal CXRs and integrated RNN models to forecast ED wait times, improving pandemic resource planning.
Real-time Echocardiography Image Analysis
Co-invented a patent-filed AI system estimating cardiac parameters (IVC collapsibility, RAP) in real time from echo frames, achieving expert-level segmentation and quantification.
Latent Diffusion-Based Augmentation for Pediatric TB
Fine-tuned latent diffusion models to synthesize high-resolution pediatric CXRs displaying TB patterns, augmenting scarce datasets and boosting TB detection ROC‑AUC from 0.84 to 0.92.
AI-Driven Pediatric Screening via Chest X‑Rays
Engineered CNN–Vision Transformer ensembles with modality-specific pretext learning for pneumonia and TB detection in pediatric CXRs, introducing ROI visualization to reduce bias.
Population-Scale TB Screening
Applied ensemble methods and Monte Carlo Dropout for uncertainty quantification in TB lesion detection across frontal and lateral CXRs, aligning with NIAID elimination goals.
Cervical Cancer ML Screening at Scale
Developed a deep-learning ensemble for automated cervical image quality assessment, enhancing screening reliability for large-scale deployment.
Publication Metrics
High-Impact Publications
- Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images – 607 citations (2018)
- Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays – 396 citations (2020)
- Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs – 357 citations (2018)
- Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images – 197 citations (2019)
- Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs – 146 citations (2020)
- Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays – 95 citations (2020)
- Visual interpretation of convolutional neural network predictions in classifying medical image modalities – 93 citations (2019)
- A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs – 90 citations (2019)
- Advances in deep learning for tuberculosis screening using chest X-rays: the last 5 years review – 82 citations (2022)
- Malaria Screener: a smartphone application for automated malaria screening – 76 citations (2020)
- Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs – 66 citations (2019)
- Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images – 65 citations (2018)
- Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs – 63 citations (2020)
- Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings – 61 citations (2021)
- Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation – 61 citations (2018)
- NLM at ImageCLEF 2018 Visual Question Answering in the Medical Domain – 60 citations (2018)
- Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks – 58 citations (2022)
- Improved semantic segmentation of tuberculosis—consistent findings in chest x-rays using augmented training of modality-specific u-net models with weak localizations – 57 citations (2021)
- Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection – 54 citations (2020)
- Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs – 53 citations (2019)
Global Interaction (Past 5 Years)
| Year | Details |
|---|---|
| 2025 | Member of the Technical Program Committee, 3rd International Conference on Deep Learning Theory and Applications (DeLTA 2025), Bilbao, Spain, 13–14 June 2025. Link |
| 2024 | Member of the Technical Program Committee, International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, 03–06 December 2024. Link |
| 2023 | Member of the Technical Program Committee, 18th International Conference on Computer Vision Theory and Applications (VISAPP-2023), Lisbon, Portugal, 22–24 February 2023. Link |
| 2023 | Member of the Technical Program Committee, 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023), Lisbon, Portugal, 22–24 February 2023. Link |
| 2023 | Member of the Technical Program Committee, 7th International Work-Conference on Artificial Neural Networks (IWANN2023), Ponta Delgada, Azores, Portugal, June 2023. |
| 2023 | Member of the Technical Program Committee, 4th International Conference on Image Processing and Capsule Networks (ICIPCN–2023), Bangkok, Thailand, 10–11 August 2023. Link |
| 2023 | Member of the Technical Program Committee, International Conference on Self-Sustainable Artificial Intelligence Systems (ICSSAS 2023), Erode, India, 18–20 October 2023. Link |
| 2023 | Member of the Technical Program Committee and Keynote Speaker, 3rd International Conference on Artificial Intelligence and Knowledge Processing (AIKP'23), Woxsen University, Telangana, India, 6–8 October 2023. Link |
| 2023 | Member of the Technical Program Committee, 1st International Conference on Current Advancements in Machine Learning (ICCAML2024), SICSR, Pune, India, 28–29 February 2024. Link |
| 2022 | Member of the Technical Program Committee, 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022), Online, 6–8 February 2022. Link |
| 2021 | Member of the Technical Program Committee, 1st International Conference on Artificial Intelligence and Knowledge Processing (AIKP'21), Woxsen University, Telangana, India, 24 April 2021. Link |
Awards & Grants
Patent Application
- U.S. Patent Application No. 63/350,720: “Real-time AI for Open-world and Explainable Echocardiography Analysis and Quantification” (filed June 9, 2022).
Honors & Awards
- NIH Director’s Award (2023)
- Best Employee Award, Medical Science & Computing LLC (2020)
- SAS Group Award, NIH/NLM (2018)
- IEEE Best Project Award, AT&T Bell Labs (2000)
Technical Skills
Python · PyTorch · TensorFlow · Keras · MATLAB
Professional Membership
- SPIE (Life Member)
- IEEE (Senior Member)
- IEEE EMBS (Senior Member)