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Results: 13
Number of items: 13
  • Open Access
    Stam, O. J. M., Francis, K. J., & Awasthi, N. (2025). PA OmniNet: A retraining-free, generalizable deep learning framework for robust photoacoustic image reconstruction. Photoacoustics, 45, Article 100740. https://doi.org/10.1016/j.pacs.2025.100740
  • Open Access
    Barratov, F., Rajendran, P., Singh, M. K. A., Francis, K. J., & Awasthi, N. (2025). Enhancing signal-to-noise ratio in LED-based photoacoustic imaging using Conditional Denoising Diffusion Probabilistic Model. In A. A. Oraevsky, & L. V. Wang (Eds.), Photons Plus Ultrasound: Imaging and Sensing 2025: 26–29 January 2025, San Francisco, California, United States Article 13319 23 (Proceedings of SPIE; Vol. 13319), (Progress in Biomedical Optics and Imaging; Vol. 26, No. 28). SPIE. https://doi.org/10.1117/12.3045335
  • Open Access
    Kumar, K. N., Mohan, C. K., Cenkeramaddi, L. R., & Awasthi, N. (2025). Minimal data poisoning attack in federated learning for medical image classification: An attacker perspective. Artificial Intelligence in Medicine, 159, Article 103024. https://doi.org/10.1016/j.artmed.2024.103024
  • Open Access
    De Santi, B., Awasthi, N., & Manohar, S. (2024). Using denoising diffusion probabilistic models to enhance quality of limited-view photoacoustic tomography. In A. A. Oraevsky, & L. V. Wang (Eds.), Photons Plus Ultrasound: Imaging and Sensing 2024: 28–31 January 2024, San Francisco, California, United States Article 1284213 (Proceedings of SPIE; Vol. 12842), (Progress in Biomedical Optics and Imaging; Vol. 25, No. 27). SPIE. https://doi.org/10.1117/12.3001616
  • 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
    Maas, E. J., Awasthi, N., van Pelt, E. G., van Sambeek, M. R. H. M., & Lopata, R. G. P. (2024). Automatic Segmentation of Abdominal Aortic Aneurysms From Time-Resolved 3-D Ultrasound Images Using Deep Learning. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 71(11), 1420-1428. https://doi.org/10.1109/TUFFC.2024.3389553
  • Open Access
    Barkhof, F., Abbring, S., Pardasani, R., & Awasthi, N. (2024). Deep learning based tumor detection and segmentation for automated 3D breast ultrasound imaging. In 2024 IEEE South Asian Ultrasonics Symposium conference proceedings (SAUS): 27-29 March 2024 (pp. 21-24). IEEE. https://doi.org/10.1109/saus61785.2024.10563487
  • Open Access
    Loos, V., Pardasani, R., & Awasthi, N. (2024). Demystifying the effect of receptive field size in U-Net models for medical image segmentation. Journal of Medical Imaging, 11(5), Article 054004. https://doi.org/10.1117/1.jmi.11.5.054004
  • Open Access
    Chel, A., Gonggrijp, M., Kyriacou, V., Retamal Guiberteau, V., Latorre Moreno, L., & Awasthi, N. (2024). Automatic Segmentation of Cardiac Structures from 2D Echocardiographic Images using Transformers. In 2024 IEEE South Asian Ultrasonics Symposium conference proceedings (SAUS): 27-29 March 2024 (pp. 29-32). IEEE. https://doi.org/10.1109/saus61785.2024.10563657
  • 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
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