Gupta, U., Paluru, N., Nankani, D., Kulkarni, K., & Awasthi, N. (2024). A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. Heliyon, 10(5), Article e26787. https://doi.org/10.1016/j.heliyon.2024.e26787
2023
Awasthi, N., van Anrooij, L., Jansen, G., Schwab, H. M., Pluim, J. P. W., & Lopata, R. G. P. (2023). Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques. Healthcare (Switzerland), 11(1), Article 123. https://doi.org/10.3390/healthcare11010123[details]
Awasthi, N., Vermeer, L., Fixsen, L. S., Lopata, R. G. P., & Pluim, J. P. W. (2022). LVNet: Lightweight Model for Left Ventricle Segmentation for Short Axis Views in Echocardiographic Imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(6), 2115-2128. https://doi.org/10.1109/TUFFC.2022.3169684[details]
Awasthi, N., Dayal, A., Cenkeramaddi, L. R., & Yalavarthy, P. K. (2021). Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68(6), 2023-2037. Article 9383274. https://doi.org/10.1109/TUFFC.2021.3068190
Awasthi, N., Gupta, S., Kiran, A., & Pardasani, R. (2021). State-of-the-art equipment for rapid and accurate diagnosis of COVID-19. In V. E. Balas, O. German, G. Wang, M. Arif, & O. A. Postolache (Eds.), Biomedical Engineering Tools for Management for Patients with COVID-19 (pp. 19-40). Academic Press. https://doi.org/10.1016/B978-0-12-824473-9.00012-4
Awasthi, N., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2021). Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data. Biomedical optics express, 12(3), 1320-1338. https://doi.org/10.1364/BOE.415182
Awasthi, N., Pardasani, R., & Gupta, S. (2021). Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans. In A. Crimi, & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers (pp. 168-178). (Lecture Notes in Computer Science; Vol. 12659). Springer. https://doi.org/10.1007/978-3-030-72087-2_15
Jansen, G., Awasthi, N., Schwab, H. M., & Lopata, R. (2021). Enhanced Radon Domain Beamforming Using Deep-Learning-Based Plane Wave Compounding. In 2021 IEEE International Ultrasonics Symposium (IUS) IEEE Computer Society. https://doi.org/10.1109/IUS52206.2021.9593731
Katare, P., Awasthi, N., Venukumar, A., & Gorthi, S. S. (2021). Low-Cost, Continuous Motion Imaging, Computationally Augmented Whole Slide Imager for Digital Pathology. IEEE Journal of Selected Topics in Quantum Electronics, 27(4), Article 9384204. https://doi.org/10.1109/JSTQE.2021.3067389
Kulkarni, K., Awasthi, N., Roberts, J. D., & Armoundas, A. A. (2021). Utility of a Smartphone-Based System (cvrPhone) in Estimating Minute Ventilation from Electrocardiographic Signals. Telemedicine and e-Health, 27(12), 1433-1439. https://doi.org/10.1089/tmj.2020.0507
Wu, M., Awasthi, N., Rad, N. M., Pluim, J. P. W., & Lopata, R. G. P. (2021). Advanced ultrasound and photoacoustic imaging in cardiology. Sensors, 21(23), Article 7947. https://doi.org/10.3390/s21237947
2020
Awasthi, N., Jain, G., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2020). Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2660-2673. Article 9018129. https://doi.org/10.1109/TUFFC.2020.2977210
Awasthi, N., Katare, P., Gorthi, S. S., & Yalavarthy, P. K. (2020). Guided filter based image enhancement for focal error compensation in low cost automated histopathology microscopic system. Journal of Biophotonics, 13(11), Article e202000123. https://doi.org/10.1002/jbio.202000123
Pardasani, R., & Awasthi, N. (2020). Classification of 12 Lead ECG Signal Using 1D-Convolutional Neural Network with Class Dependent Threshold. In 2020 Computing in Cardiology (CinC 2020): Rimini, Italy, 13-16 September 2020 (pp. 321-324). (Computing in Cardiology; Vol. 47). IEEE. https://doi.org/10.22489/CinC.2020.277
Pardasani, R., Chaudhuri, R., Awasthi, N., & Goel, M. (2020). Machine Learning and Deep Learning Approaches to Quantify Respiratory Distress Severity and Predict Critical Alarms. In 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020 Article 9374301 (2020 IEEE International Conference on Healthcare Informatics, ICHI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI48887.2020.9374301
Pardasani, R., Chaudhuri, R., Awasthi, N., Chaurasia, S., & Maya, S. (2020). Quantitative Assessment of Respiratory Distress Using Convolutional Neural Network for Multivariate Time Series Segmentation. In 2020 Computing in Cardiology (CinC 2020): Rimini, Italy, 13-16 September 2020 (pp. 465-468). (Computing in Cardiology; Vol. 47). IEEE. https://doi.org/10.22489/CinC.2020.271
2019
Awasthi, N., Prabhakar, K. R., Kalva, S. K., Pramanik, M., Babu, R. V., & Yalavarthy, P. K. (2019). PA-Fuse: Deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics. Biomedical optics express, 10(5), 2227-2243. Article #357458. https://doi.org/10.1364/BOE.10.002227
2018
Awasthi, N., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2018). Image-guided filtering for improving photoacoustic tomographic image reconstruction. Journal of Biomedical Optics, 23(9), Article 091413. https://doi.org/10.1117/1.JBO.23.9.091413
Awasthi, N., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2018). Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography. Journal of Biomedical Optics, 23(7), Article 071204. https://doi.org/10.1117/1.JBO.23.7.071204
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