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Verantwoorde Digitale Transformaties VDT

N. (Navchetan) Awasthi

Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
  • Science Park 904
Postadres
  • Postbus 94323
    1090 GH Amsterdam
Contactgegevens
  • Publicaties

    2025

    • 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 [details]
      • Downloads
    • 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 [details]

    2024

    • Barkhof, F., Abbring, S., Pardasani, R., & Awasthi, N. (2024). Deep learning based tumor detection and segmentation for automated 3D breast ultrasound imaging. In SAUS 2024 - IEEE South Asian Ultrasonics Symposium, Proceedings IEEE. https://doi.org/10.1109/saus61785.2024.10563487
    • Chel, A., Gonggrijp, M., Kyriacou, V., Retamal Guiberteau, V., Moreno, L. L., & Awasthi, N. (2024). Automatic Segmentation of Cardiac Structures from 2D Echocardiographic Images using Transformers. In SAUS 2024 - IEEE South Asian Ultrasonics Symposium, Proceedings IEEE. https://doi.org/10.1109/saus61785.2024.10563657
    • 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 [details]
    • Gholampour, A., Francis, K. J., Wu, M., Rad, N. M., Lopata, R. G. P., & Awasthi, N. (2024). Deep Learning-Based Methods for Photoacoustic Imaging Reconstruction: Concepts, Promises, Pitfalls, and Futures. In Biomedical Photoacoustics (pp. 155-177). Springer. https://doi.org/10.1007/978-3-031-61411-8_5
    • 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
    • Joshi, R. C., Awasthi, N., Parida, P., & Saikia, M. J. (2024). Corrigendum: Editorial: Physiological signal processing for wellness. Frontiers in Signal Processing, 4, Article 1496320. https://doi.org/10.3389/frsip.2024.1496320
    • Joshi, R. C., Awasthi, N., Parida, P., & Saikia, M. J. (2024). Editorial: Physiological signal processing for wellness. Frontiers in Signal Processing, 4. https://doi.org/10.3389/frsip.2024.1391335
    • 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
    • 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
    • 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
    • Nievergeld, A., Çetinkaya, B., Maas, E., van Sambeek, M., Lopata, R., & Awasthi, N. (2024). Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images. Medical & Biological Engineering & Computing. Advance online publication. https://doi.org/10.1007/s11517-024-03216-7

    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]

    2022

    • 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]

    2021

    • 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. Computing in Cardiology, 47, 321-324. 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 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. Computing in Cardiology, 47, 465-468. 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
    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
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