Expertise and Research Interests:

Ghada Zamzmi joined the Communication Engineering Branch at Lister Hill National Center for Biomedical Communications (LHNCBC) in February 2019. She received her PhD degree from University of South Florida in 2018. She focuses on using computational sciences and engineering techniques toward advancing healthcare of vulnerable populations (e.g., infants, minority groups). Ghada research interests include medical imaging, affective and cognitive computing, human behavior analysis, and healthcare application. She has >12 journal papers published in top tier journals (e.g., Transactions on Affective Computing), >15 conference publications and two patents. She served in the program committee in several top conferences in Computer Vision including CVPR, NeurIPS, and MICCAI. She chaired several academic workshops and events in her area of interests, led and participated in several mentoring programs. Ghada received different prestigious awards such as MIT Innovator under 35 and IEEE Computational Life Sciences best PhD Thesis Award (2019). She’s selected as the North America Ambassador for the international organization Women in AI.

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.

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.

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.

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, 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.

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, 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, 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].

Zamzmi G, Hsu L, Li W, Sachdev V, Antani SK. Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions. IEEE Reviews in Biomedical Engineering, doi: 10.1109/RBME.2020.2988295

Zamzmi G, Hsu L, Li W, Sachdev V, Antani SK. Fully automated spectral envelope and peak velocity detection from Doppler echocardiography images. Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113144G (16 March 2020); https://doi.org/10.1117/12.2551183

Zamzmi G, Hsu L, Li W, Sachdev V, Antani SK. Echo Doppler Flow Classification and Goodness Assessment with Convolutional Neural Networks. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 1744-1749, doi: 10.1109/ICMLA.2019.00283

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.