Expertise and Research Interests:

Dr. Sivaramakrishnan Rajaraman is an accomplished Research Scientist contributing to medical image processing ML/AI at the National Library of Medicine (NLM), National Institutes of Health (NIH), USA. His work revolves around harnessing computational sciences and engineering techniques to revolutionize automated medical decision-making. Dr. Rajaraman’s diverse research portfolio spans machine learning, data science, biomedical image analysis, and computer vision. Before joining NLM, he had 15 years of academic experience where he taught core and allied subjects in electronics, communication, and biomedical engineering while publishing extensively in national and international journals and conferences with a cumulative h-index of 24 (to date). Dr. Rajaraman serves on the Editorial Boards of premier journals like PeerJ Computer Science, PLOS ONE, PLOS Digital Health, and MDPI. He is actively involved in organizing special issues and conference workshops. He is reviewing manuscripts for over 100 prestigious journals and conferences and is holding memberships in SPIE, IEEE, and BMES, demonstrating his commitment to excellence in his field.

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:

Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Semantically redundant training data removal and deep model classification performance: A study with chest X-rays. Computerized Medical Imaging and Graphics. Volume 115, 2024, 102379, ISSN 0895-6111, https://doi.org/10.1016/j.compmedimag.2024.102379.

Liang Z, Xue Z, Rajaraman S, Antani SK. Covid-19 Pneumonia Chest X-Ray Pattern Synthesis by Stable Diffusion. 2024 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, 2024, pp. 21-24, doi: 10.1109/SSIAI59505.2024.10508671.

Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images. PLOS Digital Health 3(1): e0000286. https://doi.org/10.1371/journal.pdig.0000286.

Liang Z, Xue Z, Feng Y, Rajaraman S, Huang JX, Antani SK. Emergency Department Wait Time Forecast based on Semantic and Time Series Patterns in COVID-19 Pandemic. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3067-3072, doi: 10.1109/BIBM58861.2023.10385758.

Mahmoodi E, Xue Z, Rajaraman S, Antani SK. A Study on Reducing Big Data Image Annotation Burden Through Iterative Expert-In-The-Loop Strategy. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3097-3102, doi: 10.1109/BIBM58861.2023.10385356.

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani S. Can deep adult lung segmentation models generalize to the pediatric population? Expert Systems with Applications, Volume 229, Part A, 2023, 120531, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120531.

Liang Z, Xue Z, Rajaraman S, Feng Y, Antani SK. Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning. In: Xue Z, et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_12.

Zamzmi G, Hsu LY, Rajaraman S, Li W, Sachdev V, Antani S. Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure. Int J Cardiovasc Imaging. 2023 Sep 8. doi: 10.1007/s10554-023-02941-8. Epub ahead of print. PMID: 37682418.

Oguguo T, Zamzmi G, Rajaraman S, Yang F, Xue Z, Antani S. A Comparative Study of Fairness in Medical Machine Learning. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5, doi: 10.1109/ISBI53787.2023.10230368.

Xue Z, Yang F, Rajaraman S, Zamzmi G, Antani S. Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection. Diagnostics. 2023; 13(6):1068. https://doi.org/10.3390/diagnostics13061068.

Yang F, Zamzmi G, Angara S, Rajaraman S, Aquilina A, Xue Z, Jaeger S, Papagiannakis E, Antani SK. Assessing Inter-Annotator Agreement for Medical Image Segmentation. IEEE Access, doi: 10.1109/ACCESS.2023.3249759.

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani SK. Assessing the Impact of Image Resolution on Deep Learning for TB Lesion Segmentation on Frontal Chest X-rays. Diagnostics. 2023; 13(4):747. https://doi.org/10.3390/diagnostics13040747.

Rajaraman S, Zamzmi G, Yang F, Xue Z, Antani SK. Data Characterization for Reliable AI in Medicine. Recent Trends Image Process Pattern Recogn (2022). 2023;1704:3-11. doi: 10.1007/978-3-031-23599-3_1. Epub 2023 Jan 11. PMID: 36780238; PMCID: PMC9912175.

Rajaraman S, Antani SK. Editorial on Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”. Diagnostics. 2022; 12(11):2615. https://doi.org/10.3390/diagnostics12112615.

Santosh K, Allu S, Rajaraman S, Antani SK. Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review. J Med Syst 46, 82 (2022). https://doi.org/10.1007/s10916-022-01870-8.

Xue Z, Angara S, Guo P, Rajaraman S, Jeronimo J, Rodriguez AC, Alfaro K, Charoenkwan K, Mungo C, Fokom‐Domgue J, Wentzensen N, Desai K, Ajenifuja K, Wikström E, Befano BS, Antani S. Image Quality Classification for Automated Visual Evaluation of Cervical Precancer. Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_20.

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani SK. A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs. Bioengineering. 2022; 9(9):413. https://doi.org/10.3390/bioengineering9090413.

Zamzmi G, Rajaraman S, Hsu LY, Sachdev V, Antani S. Real-time echocardiography image analysis and quantification of cardiac indices. Med Image Anal. 2022 Aug;80:102438. doi: 10.1016/j.media.2022.102438. Epub 2022 Jun 9. PMID: 35868819; PMCID: PMC9310146.

Yang F, Lu PX, Deng M, Wáng YXJ, Rajaraman S, Xue Z, Folio LR, Antani SK, Jaeger S. Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases. Data 2022, 7, 95. https://doi.org/10.3390/data7070095.

Rajaraman S, Guo P, Xue Z, Antani SK. A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays. Diagnostics (Basel). 2022 Jun 11;12(6). doi: 10.3390/diagnostics12061442. PubMed PMID: 35741252; PubMed Central PMCID: PMC9221627.

Rajaraman S, Zamzmi G, Yang F, Xue Z, Jaeger S, Antani SK. Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays. Biomedicines 2022, 10, 1323. https://doi.org/10.3390/biomedicines10061323.

Rajaraman S, Cohen G, Spear L, Folio L, Antani S. DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs. PLOS ONE 17(3): e0265691. https://doi.org/10.1371/journal.pone.0265691

Rajaraman S, Zamzmi G, Folio LR, Antani S. Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers. Front Genet. 2022;13:864724. doi: 10.3389/fgene.2022.864724. eCollection 2022. PubMed PMID: 35281798; PubMed Central PMCID: PMC8907925.

Oguguo T, Zamzmi G, Rajaraman S, Antani S. Open-world active learning for echocardiography view classification. SPIE Medical Imaging, (February 2022).

Rajaraman S, Ganesan P, Antani S. Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks. PLoS ONE 17(1): e0262838. https://doi.org/10.1371/journal.pone.0262838.

Rajaraman S, Zamzmi G, Antani SK. Novel loss functions for ensemble-based medical image classification. PLoS ONE 16(12): e0261307.

Zamzmi G, Rajaraman S, Sachdev V, Antani S. Trilateral Attention Network for Real-Time Cardiac Region Segmentation. IEEE Access, vol. 9, pp. 118205-118214, 2021, doi: 10.1109/ACCESS.2021.3107303.

Rajaraman S, Zamzmi G, Folio L, Alderson P, Antani S. Chest X-ray bone suppression for improving classification of tuberculosis-consistent findings. Diagnostics. 2021; 11(5):840. https://doi.org/10.3390/diagnostics11050840.

Zamzmi G, Rajaraman S, Antani S. UMS-Rep: Unified Modality-Specific Representation for Efficient Medical Image Analysis. Informatics in Medicine Unlocked, 24, art. no. 100571. http://www.journals.elsevier.com/informatics-in-medicine-unlocked doi: 10.1016/j.imu.2021.100571

Rajaraman S, Zamzmi G, Folio L, Alderson P, Antani S. Improved TB classification using bone-suppressed chest radiographs. arXiv preprint arXiv:2104.04518 [eess.IV].

Rajaraman S, Folio LR, Dimperio J, Alderson PO, Antani SK. Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations. Diagnostics (Basel). 2021 Mar 30;11(4):616. doi: 10.3390/diagnostics11040616.

Rajaraman S, Folio L, Dimperio J, Alderson P, Antani S. Training custom modality-specific U-Net models with weak localizations for improved Tuberculosis segmentation and localization. arXiv preprint arXiv:2102.10607 [cs.CV].

Rajaraman S, Sornapudi S, Alderson PO, Folio LR, Antani SK. Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PLoS ONE 15(11): e0242301.

Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, Palaniappan K, Antani S, Maude RJ, Jaeger S. Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020 Nov 11;20(1):825. doi: 10.1186/s12879-020-05453-1.

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.

Zamzmi G, Rajaraman S, Antani SK. Unified Representation Learning for Efficient Medical Image Analysis. arXiv:2006.11223 [cs.CV] Under Review

Zamzmi G, 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.

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.

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, 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.

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).

Jaeger S, Antani SK, Rajaraman S, Yang F, Yu H. Malaria Screening: Research into Image Analysis and Deep Learning. Report to the Board of Scientific Counselors September 2018.

Rajaraman S, Silamut K, Hossain MA, Ersoy I, Maude RJ, Jaeger S, Thoma GR, Antani SK. Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images. J Med Imaging (Bellingham). 2018 Jul;5(3):034501. doi: 10.1117/1.JMI.5.3.034501. Epub 2018 Jul 18.

Rajaraman S, Candemir S, Xue Z, Alderson P, Kohli M, Abuya J, Thoma GR, Antani SK. A novel stacked generalization of models for improved TB detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC 2018), Honolulu, Hawaii, 2018. pp. 718-721.

Thamizhvani TR, Lakshmanan S, Rajaraman S. Mobile application-based computer-aided diagnosis of skin tumours from dermal images. The Imaging Science Journal, 66:6, 382-391, 2018, DOI: 10.1080/13682199.2018.1492682

Xue Z, Rajaraman S, Long LR, Antani SK, Thoma GR. Gender Detection from Spine X-ray Images Using Deep Learning. Proc. IEEE International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. pp. 54-58, DOI:10.1109/CBMS.2018.00017.

Rajaraman S, Antani SK, Poostchi M, Silamut K, Hossain MA, Maude RJ, Jaeger S, Thoma GR. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ. 2018 Apr 16;6:e4568. doi: 10.7717/peerj.4568. PMID: 29682411; PMCID: PMC5907772.

Rajaraman S, Antani SK, Candemir S, Xue Z, Abuya J, Kohli M, Alderson P, Thoma GR. Comparing deep learning models for population screening using chest radiography. Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751E (27 February 2018).

Thamizhvani TR, Lakshmanan S, Rajaraman S. Computer Aided Diagnosis of Skin Tumours from Dermal Images. . Hemanth D., Smys S. (eds) Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham

Rajaraman S, Antani SK, Xue Z, Candemir S, Jaeger S, Thoma GR. Visualizing abnormalities in chest radiographs through salient network activations in Deep Learning. Proc. IEEE Life Sciences Conference (LSC), Sydney, Australia, 2017. pp. 71-74, DOI:10.1109/LSC.2017.8268146.

Rajaraman S, Antani SK, Jaeger S. Visualizing Deep Learning Activations for Improved Malaria Cell Classification. Proceedings of The First Workshop in Medical Informatics and Healthcare (MIH 2017), Proceedings of Machine Learning Research (PMLR), v. 69, p. 40-47.