Awards and Publications

The awards section of this page lists grants that are currently funding this project. The publications section contains a list of papers that were published as a part of this project.

Awards

NSF RAPID: Deep Learning Models for Early Screening of COVID-19 using CT Images

Publications

Aryya Gangopadhyay, Michael Morris, Babak Saboury, Eliot L. Siegel, Yelena Yesha: IDIOMS: Infectious Disease Imaging Outbreak Monitoring System. Digit. Gov. Res. Pract. 2(1): 15:1-15:5 (2021).
https://dl.acm.org/doi/abs/10.1145/3428092
Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay, et al. "Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network". In: Proceedings of International Conference on Computational Science and Computational Intelligence. IEEE. 2020.
https://arxiv.org/abs/2104.02060
Presentation Video:
S. Menon et al., "Generating Realistic COVID-19 x-rays with a Mean Teacher + Transfer Learning GAN," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 1216-1225, doi: 10.1109/BigData50022.2020.9377878.
https://ieeexplore.ieee.org/document/9377878
Presentation Video:
Mangalagiri, J., Sugumar, J. S., Menon, S., Chapman, D., Yesha, Y., Gangopadhyay, A., ... & Nguyen, P. (2021, December). Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4387-4395). IEEE.
https://ieeexplore.ieee.org/document/9671656
Presentation Video:
Menon, S., Mangalagiri, J., Galita, J., Morris, M., Saboury, B., Yesha, Y., ... & Chapman, D. (2021). CCS-GAN: COVID-19 CT-scan classification with very few positive training images. arXiv preprint arXiv:2110.01605.
https://arxiv.org/pdf/2110.01605.pdf
Shivadekar, S., Mangalagiri, J., Nguyen, P., Chapman, D., Halem, M., & Gite, R. (2021, August). An Intelligent Parallel Distributed Streaming Framework for near Real-time Science Sensors and High-Resolution Medical Images. In 50th International Conference on Parallel Processing Workshop (pp. 1-9).
https://dl.acm.org/doi/10.1145/3458744.3474039


COVID-19 Project
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Awards and Publications The awards section of this page lists grants that are currently funding this project. The publications section contains a list of papers that were published as a part of this project. Awards NSF RAPID: Deep Learning Models for Early Screening of COVID-19 using CT Images Publications Aryya Gangopadhyay, Michael Morris, Babak Saboury, Eliot L. Siegel, Yelena Yesha: IDIOMS: Infectious Disease Imaging Outbreak Monitoring System. Digit. Gov. Res. Pract. 2(1): 15:1-15:5 (2021). https://dl.acm.org/doi/abs/10.1145/3428092 Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay, et al. "Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network". In: Proceedings of International Conference on Computational Science and Computational Intelligence. IEEE. 2020. https://arxiv.org/abs/2104.02060 Presentation Video: S. Menon et al., "Generating Realistic COVID-19 x-rays with a Mean Teacher + Transfer Learning GAN," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 1216-1225, doi: 10.1109/BigData50022.2020.9377878. https://ieeexplore.ieee.org/document/9377878 Presentation Video: Mangalagiri, J., Sugumar, J. S., Menon, S., Chapman, D., Yesha, Y., Gangopadhyay, A., ... & Nguyen, P. (2021, December). Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4387-4395). IEEE. https://ieeexplore.ieee.org/document/9671656 Presentation Video: Menon, S., Mangalagiri, J., Galita, J., Morris, M., Saboury, B., Yesha, Y., ... & Chapman, D. (2021). CCS-GAN: COVID-19 CT-scan classification with very few positive training images. arXiv preprint arXiv:2110.01605. https://arxiv.org/pdf/2110.01605.pdf Shivadekar, S., Mangalagiri, J., Nguyen, P., Chapman, D., Halem, M., & Gite, R. (2021, August). An Intelligent Parallel Distributed Streaming Framework for near Real-time Science Sensors and High-Resolution Medical Images. In 50th International Conference on Parallel Processing Workshop (pp. 1-9). https://dl.acm.org/doi/10.1145/3458744.3474039
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