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Results: 15
Number of items: 15
  • Open Access
    Kowalchuk, M. A., Gupta, S., & Awasthi, N. (2024). Applying pre-trained deep learning models for Multi-Label Classification of Realistic and Noisy Electrocardiogram Images. Computing in Cardiology, 51. https://doi.org/10.22489/cinc.2024.496
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • Jansen, G., Awasthi, N., Schwab, H.-M., & Lopata, R. (2021). Enhanced Radon Domain Beamforming Using Deep-Learning-Based Plane Wave Compounding. In IEEE IUS 2021: International Ultrasonics Symposium : virtual symposium, September 11-16, 2021 : 2021 symposium proceedings (pp. 1899-1903). IEEE. https://doi.org/10.1109/IUS52206.2021.9593731
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